WO2015189745A1

WO2015189745A1 - A computer implemented system and method for predicting and distributing online content

Info

Publication number: WO2015189745A1
Application number: PCT/IB2015/054219
Authority: WO
Inventors: Mandar Agashe
Original assignee: Mandar Agashe
Priority date: 2014-06-09
Filing date: 2015-06-04
Publication date: 2015-12-17

Abstract

A computer implemented online music platform and method for predicting and distributing an online content is disclosed. The platform includes a listener repository, an artist repository, an advertisement repository and a prediction and distribution module. The listener repository stores listener related information. The artist repository stores artist related information. The advertisement repository stores online contents such as advertisements. The prediction and distribution module communicates with listener repository and determines the listener's profile information. The prediction and distribution module communicates with advertisement repository and categorize the advertisements. The prediction and distribution module maps the categorized advertisements with the listener's profile information and distribute the advertisements to the listener based on the listener's information.

Description

A COMPUTER IMPLEMENTED SYSTEM AND METHOD FOR PREDICTING AND DISTRIBUTING ONLINE CONTENT

This patent application is a patent of addition to Indian patent application No. 112/MUM/2013 filed on January 14, 2013, the contents of which are specifically incorporated herein by reference.

TECHNICAL FIELD

The present invention generally relates to web-based distribution of digital content and more particularly to the system and method for predicting and distributing an online content.

DEFINITIONS OF TERMS USED IN THE COMPLETE SPECIFICATION

The expression 'listener' used hereinafter in the specification refers to but is not limited to a registered user, a customer, an admirer, an audience, a hearer, a fan, an onlooker, a follower, and a viewer.

The expression 'artist' used hereinafter in the specification refers to but is not limited to a musician, a composer, a singer, a lyrist, a writer, an instrumentalist, an entertainer, a performer, a player, a session player, a soloist, a virtuoso, a vocalist, a poet, and a creative person.

The expression 'music piece' used hereinafter in the specification refers to but is not limited to a track, a song, an audio clip, a video clip, a poetry, a rhythm, a composition, a lyrics, a remix, an entertainment, an item, a record, a sound track, a vocal track, a tune, a ballad, a chant, an anthem, an expression, a movie album, a movie music, a music, a sound-stripe and combinations thereof.

The expression 'device' used hereinafter in the specification refers to but is not limited to a mobile phone, a cell phone, a laptop, a tablet, a desktop, an iPad, a Personal Digital Assistant (PDA), a notebook, a net book and the like, including a wired and/or a wireless computing device. The expression 'advertisement' used hereinafter in the specification refers to but is not limited to an image advertisement, an audio advertisement, a video advertisement and a combination thereof.

These definitions are in addition to those expressed in the art.

BACKGROUND

Music is intrinsic to all cultures and can have surprising benefits such as improving memory and focusing attention, but also for physical coordination and development. Listening music has become a part of everyday life now. The web based technology has enhanced the accessibility of the information just by tapping the mouse. Information related to data, music content, such as television, movies, and other audio and video content are available on the web. Frequently, the web is utilized to transmit digital content from one entity to another. Furthermore, a user may also access the music content over the web through an online store, an Internet radio station, online music service, online movie service, online music platform and the like.

An advertiser may use an online advertising to accomplish business goals. The advertiser launch advertising campaigns intended to attract the users. However, such efforts are inefficient. The demand for accessing the music content online continues to surge. The user is actively engaged with accessing online music from an online music platform. Therefore, there is a need to distribute the online advertisements to the user by using the online music platform. Further, there is also a need to distribute the online advertisements to the user based on the user behavioural attributes stored in the online music platform.

Therefore, there is felt a need for a computer implemented solution that facilitates in distribution of online content to the user based on customer behavioural attributes.

OBJECTS

An object of the present disclosure is to provide a system and method for predicting and distributing an online content. Another object of the present disclosure is to provide a platform that predicts a user's likes and dislikes based on the user's preferences.

Still another object of the present disclosure is to provide a platform that pushes advertisement related information to the users based on the prediction of the user's like and dislike.

Still another object of the present disclosure is to provide a platform that pushes advertisement related information based on the user's Global Positioning System (GPS) location.

Still another object of the present disclosure is to categorize the advertisements based on the genres.

SUMMARY

This summary is provided to introduce concepts related to predicting and distributing online content, which is further described below in the detailed description. This summary is neither intended to identify essential features of the present disclosure nor is it intended for use in determining or limiting the scope of the present disclosure.

In an embodiment, method and platform for predicting and distributing online content is disclosed. The method may include storing listener registration information and listener profile information in a listener repository. The listener profile information may include the listener behavioral attributes such as likes, dislikes, and the interest related information, hobbies, permanent address, temporary address, music preferences, favorite artists, age, contact details and sex. The method may further include storing registration information corresponding to users registered as artists and artists profile information in an artist repository. Further, the method may include storing a plurality of advertisements in an advertisement repository. The plurality of advertisements may be stored in a format selected from a text format, an image format, an audio format, a video format and a combination format. Further, the method may include extracting the listener profile information from the listener repository. Upon extracting the listener profile information related to the listener, the method may further include determining the listener profile information, wherein the listener profile information may include at least likes and dislikes, preferences, sex, age, hobbies, music preferences, favorite artists and contact details. Further, the method may include categorizing the advertisements present in the advertising repository based on a unique predictive attributes in a real time. Further, the method may include mapping the listener profile information with the categorized advertisements. Further, the method may include aggregating the mapped advertisements corresponding to each listener and pushing the aggregated advertisements to the corresponding listener's contact details.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.

Fig. 1 illustrates a system level block diagram of the components of a computer implemented online music platform for predicting and distributing an online content, according to an implementation of the present disclosure.

Fig. 2 illustrates a network environment implementation of online music platform used for predicting and distributing online content, according to an implementation of the present disclosure.

Fig. 3 illustrates a method for predicting and distributing online content implemented in online music platform, according to an implementation of 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, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION

The present disclosure relates to a system and a method for predicting and distributing online content.

Unless specifically stated otherwise as apparent from the following discussions, it is to be appreciated that throughout the present disclosure, discussions utilizing terms such as "storing" or "extracting" or "determining" or "categorizing" or "mapping" or "aggregating" or "pushing" or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

The systems and methods are not limited to the specific embodiments described herein. In addition, modules of each system and each method can be practiced independently and separately from other modules and methods described herein. Each module and method can be used in combination with other modules and other methods.

The present disclosure envisages a computer implemented online music platform for predicting and distributing online content. The platform as disclosed in the present disclosure relates to distribution of online content such as advertisements of merchants. Guest users get registered with the online music platform either as an artist or as a listener or as an advertiser. The platform prompts the guest user to enter their login credentials to browse through the online music. The platform verifies the guest user credentials. Further, new users of the platform need to register themselves to access the online music platform. The platform distributes the online contents to the guest user based on the guest user likes/dislikes and mood.

Further, the present disclosure directed towards the listener of the online music platform. The listener of the platform accesses the platform using login credentials. The platform verifies the login credentials. The listener may create their profile in the platform and specify the personal information in their profiles such as age, sex, hobbies, permanent address, temporary address, likes and dislikes, music preferences, favourite artists and contact details. Additionally, the listener of the online music platform may specify their favourite genres in categorical manner such as first level, second level and third level.

Further, the present disclosure directed towards the advertiser. The advertiser may register with online music platform and enabled to post online contents such as advertisements by purchasing an advertisement subscription. There is provided a multiple advertisement subscription level on the online platform. The advertiser can post advertisement in a format selected from the consisting of a text format, an image format, an audio format, a video format and a combined format. Further, the present disclosure directed towards the artist. The artist of the platform accesses the platform using login credentials. The platform verifies the login credentials. The artist can create their profile in the platform and specify the personal information in their profiles such as age, sex, hobbies, permanent address, temporary address, likes and dislikes, music preferences, favorite artists and contact details.

According to an implementation, the platform extracts and determines the information related to listener such as likes and dislikes and contact details from the listener's profile. The platform categorizes the online contents in real time. The platform maps the determined information related to listener with the categorized online contents. The platform aggregates the mapped online contents corresponding to the listener. Further, the platform pushes the aggregated online contents corresponding to the listener to listener's contact details.

In another implementation, the platform detects the location of the listener using the (GPS) Global Positioning System of the listener's communication device. Based on the listener's GPS location, the platform determines the appropriate listener's profile information. The platform categorizes the online contents in real time. The platform maps the determined listener's profile information with the categorized online contents. The platform, further, aggregates the mapped online contents corresponding to the listener. Further, the platform pushes the aggregated online content corresponding to the listener to listener's contact address.

In another implementation, the platform receives a feedback on regular time intervals from the listener. The feedback from the listener is used to refine the mapping of the information related to listener with the categorized online contents.

In another implementation, the listener creates any number of music folders in the platform after registration and set preferences or permissions to receive media pieces in the listener created music folders. The listener preferences settings includes time periods such as daily, weekly, twice a month, once a month, quarterly, languages, first level genres, second level genres, third level genres, artist, location, music type and select a fan icon of particular artist. The platform facilitates the advertiser to advertise the advertisements in different listener created folders related to music, first level genres, second level genres, third level genres and displayed to the listener based on the preferences as set by him. Still yet another aspect of the present disclosure provides a non-transitory computer-readable medium having embodied thereon a computer program for executing a method. The method includes storing a listener registration information and a listener profile information in a listener repository, wherein the listener profile information comprises at least likes and dislikes, interest related information, hobbies, permanent address, temporary address, music preferences, favorite artists, age, contact details and sex. The method further includes storing registration information corresponding to users registered as artists and artists profile information in an artist repository and storing a plurality of advertisements in an advertisement repository, wherein the plurality of advertisements are stored in a format selected from at least a text format, an image format, an audio format, a video format and a combination format. Further, the method includes extracting the information related to listener from the listener repository, and determining the information related to listener from said extracted information related listener for each listener, wherein the information related to listener comprises at least likes and dislikes, preferences, sex, age, hobbies, music preferences, favorite artists and contact details, categorizing the advertisements present in the advertising repository based on a unique predictive methodology in a real time. Subsequently, the method includes mapping the information related to listener with the categorized advertisements, aggregating the mapped advertisements corresponding to each listener, and pushing the aggregated advertisements for each listener to the corresponding listener's contact details.

These and other advantages of the present subject matter would be described in greater detail in conjunction with the following figures. While aspects of described systems and methods for predicting and distributing online content may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).

Figure 1 illustrates the components involved in the online music platform 100 that provides prediction and distribution of online contents such as advertisements. The platform 100 predicts listener's likes and dislikes based on the listener's preferences. The platform 100 aims to distribute the advertisements to the listener based on their likes and dislikes so as to improve the effectiveness of the advertisements. The platform 100 includes a prediction and distribution module (102), a listener repository (122), an advertisement repository (124) and an artist repository (126). Further, the prediction and distribution module (102) includes an extractor module (120), a de terminator module (104), a mapping module (106), a location module (108), a categorization module (118), an aggregator module (116), a pushing module (114), a refinement module (110) and feedback receiver module (112).

In accordance with the present disclosure, the platform 100 includes the listener repository (122). The listener repository (122) stores the information related to listeners. The information related to listeners includes but is not restricted to listener's profile information, registration information, listener preferences and privacy settings. The listener's profile information includes but is not restricted to name, age, contact details, sex, hobbies, permanent address, listener's interest, temporary address, likes and dislikes and favourite artists. The listener preferences includes but is not restricted to time periods, artists, music type, fan icon of particular artist and languages.

In accordance with the present disclosure, the platform 100 includes the advertisement repository (124). The advertisement repository (124) stores the information related to the advertisers and advertisements. The advertisements are stored in a format but are not restricted to a text format, an image format, an audio format, a video format and combination format. An advertiser may register with the platform (100) and is enabled to post advertisements by purchasing the advertisement subscription. The advertiser may post advertisements in a format selected from the consisting of a text format, an image format, an audio format, a video format and a combination format.

In accordance with the present disclosure, the platform (100) includes the artist repository (126). The artist repository (126) stores the information related to artists. The information related to artists includes but is not restricted to artist's profile information, registration information, artist's payment information and tags created by the artists. The artist's profile information includes but is not restricted to artist's interest, name, age, contact details, sex, hobbies, permanent address, temporary address, likes and dislikes and favourite artists.

In accordance with the present disclosure, the prediction and distribution module (102) includes the extractor module (120). The extractor module (120) cooperates with the listener repository (122). The extractor module (120) extracts the information related to listener from the listener repository (122).

In accordance with the present disclosure, the prediction and distribution module (102) includes the determinator module (104). The determinator module (104) cooperates with the extractor module (120) and location module (108). The determinator module (104) receives the extracted information from the extractor module (120) and determines the information related to listener such as but not restricted to likes and dislikes, preferences, sex, hobbies, reviews posted, rating pattern, contact details from the extracted information related to listener for each listener. The determinator module (102) may determine appropriate information related to listener based on a global positioning system (GPS) location of the listener.

In accordance with the present disclosure, the prediction and distribution module (102) includes the categorization module (118). The categorization module (118) cooperates with the advertisement repository (124). The categorization module (118) is configured to access the advertisement repository (124) and categorizes the advertisements based on a unique predictive methodology in real time. The categorization of the advertisements is also based on the genres.

In accordance with the present disclosure, the prediction and distribution module (102) includes the mapping module (106). The mapping module (106) cooperates with the categorization module (118) and determinator module (104). The mapping module (106) communicates with the determinator module (104) to receive the determined information. The mapping module (106) also communicates with the categorization module (118). The mapping module (106) maps the determined information related to the listener with the categorized advertisements. The mapping module (106) may map the categorized advertisements with the determined information corresponding to each listener based on the GPS location of the listener.

In accordance with the present disclosure, the prediction and distribution module (102) includes the aggregator module (116). The aggregator module (116) cooperates with the mapping module (106). The aggregator module (116) aggregates all the mapped advertisements corresponding to each listener.

In accordance with the present disclosure, the prediction and distribution module (102) includes the pushing module (114). The pushing module (114) cooperates with the aggregator module (116). The pushing module (114) receives the aggregated advertisements related to each listener and pushes the all aggregated advertisements to the corresponding listener's contact details. In accordance with the present disclosure, the prediction and distribution module (102) includes the feedback receiver module (112). The feedback receiver module (112) cooperates with the refinement module (110). The feedback receiver module (112) receives the feedback on regular time intervals from the listeners directly by asking the listeners about current preferences, choices, listener's mood, listener's time interval to receive the advertisement information and whether the listener is satisfied with the advertisement information provided in real time. The feedback receiver module (112) communicates the listener's feedback information to the refinement tuner module (110).

In accordance with the present disclosure, the prediction and distribution module (102) includes the refinement tuner module (110). The refinement tuner module (110) cooperates with feedback receiver module (112) and mapping module (106). The refinement tuner module (110) checks the mapping of the categorized advertisements with the determined information related to listener and further refines the mapping process with the information received from the feedback receiver module (112).

In accordance with the present disclosure, prediction and distribution module (102) includes the location module (108). The location module (108) cooperates with the mapping module (106) and the determinator module (104). The location module (108) receives the location information of the listener based on the GPS information of the listener's communication device. Based on the listener's GPS location, the determinator module (104) may determine the appropriate information related to listener. The mapping module (106) may map the categorized advertisements with the determined information corresponding to each listener based on the GPS location of the listener.

Fig. 2 illustrates a network environment (204) implementing an online music platform (100), in accordance with an embodiment of the present disclosure. In said embodiment, the network environment (204) includes the platform (100). The prediction and distribution module (102) includes an extractor module (120), a determinator module (104), a mapping module (106), a location module (108), a categorization module (118), an aggregator module (116), a pushing module (114), a refinement module (110) and a feedback receiver module (112).

In one implementation, the network environment (204) may be a company network, including thousands of office personal computers, laptops, various servers, such as blade servers, and other computing devices. Examples of a company may include an information technology (IT) company, a product manufacturing company, a human resource (HR) company, a telecommunication company, or other large conglomerates. It will also be appreciated by a person skilled in the art that the company may be any company involved in any line of business. In another implementation, the network environment (204) may be a smaller private network. In yet another implementation, the network environment (204) may be a public network, such a public cloud.

The platform (100) may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the platform (100) may be included within an existing information technology infrastructure or a database management structure. Further, it will be understood that the platform (100) may be connected to a plurality of computing systems (202-1, 202-2, 202-3,..., 202-N), collectively referred to as the target computing system (202) or as an individual IT system (202). The target computing system (202) may include, but is not limited to, a desktop computer, a portable computer, a mobile phone, a handheld device, and a workstation. The target computing system (202) may be used by users, such as business users, database analysts, programmers, listener, artist, developers, data architects, software architects, module leaders, projects leaders, database administrator (DBA), stakeholders, and the like.

As shown in the figure, the target computing system (202) are communicatively coupled to the online music platform (100) over a network (204) through one or more communication links for facilitating one or more end users to access and operate the platform (100). In one implementation, the network (204) may be a wireless network, a wired network, or a combination thereof. The network (204) may also be an individual network or a collection of many such individual networks, interconnected with each other and functioning as a single large network, e.g., the Internet or an intranet. The network (204) may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network (204) 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), Transmission Control Protocol/Internet Protocol (TCP/IP), etc., to communicate with each other. Further, the network (204) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

The platform 100 further includes interface(s) (208), for example, to provide the input data in a hierarchical manner. Further, the interface(s) (208) may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. Additionally, the interface(s) (208) may enable the platform (100) to communicate with other devices, such as web servers and external repositories. The interface(s) (208) may also facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. For the purpose, the interface(s) (208) may include one or more ports.

In an implementation, the platform (100) includes a processor(s) (206) coupled to a system memory (210). The processor(s) (206) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) (206) may be configured to fetch and execute computer-readable instructions stored in the system memory (210).

The system memory (210) may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

As mentioned herein, the present disclosure provides an online music platform for predicting and distributing an online content in the computing environment. Accordingly, the prediction and distribution of the online content in a target computing system is implemented in the systems and the methods described herein.

In an implementation, the platform (100) provides the advertisements to the end user based on their profile information. The platform (100) includes the prediction and distribution module (102) configured to provide the advertisements to the end user based on their profile information. The prediction and distribution module (102) includes the extractor module (120). The extractor module extracts the user related information from the listener repository (122) and the artist repository (126).

According to the present implementation, the prediction and distribution module (102) includes the determinator module (104). The prediction and distribution module (102) also includes the location module (108). The location module (108) receives the location information of the user based on the Global Positioning System (GPS) of the user's communication device. The determinator module (104) determines the appropriate user's information based on the extracted information from the extractor module (120) or user's GPS location or combination of both.

According to the present implementation, the prediction and distribution module (102) includes the categorization module (118). The categorization module (118) categorizes the advertisements based on a unique predictive methodology in real time or the genres.

According to the present implementation, the prediction and distribution module (102) includes the mapping module (106). The mapping module (106) maps determined information related to the user with the categorized advertisements.

According to the present implementation, the prediction and distribution module (102) includes the aggregator module (116). The aggregator module (116) aggregates all the mapped advertisements corresponding to each user.

According to the present implementation, the prediction and distribution module (102) includes the pushing module (114). The pushing module (114) receives the aggregated information for each user and pushes the all aggregated advertisements to the corresponding user's contact details.

According to the present implementation, the prediction and distribution module (102) includes the feedback receiver module (112). The feedback receiver module (112) receives the feedback on regular time intervals from the users directly by asking the users about current preferences, choices, mood, and whether the user is satisfied with the advertisement information provided in real time. The feedback receiver module (112) communicates the user's feedback information to the refinement tuner module (110).

According to the present implementation, the prediction and distribution module (102) includes the refinement tuner module (110). The refinement tuner module (110) checks the mapping of the categorized advertisements with the user's determined information and further refines the mapping process with the user's information received from the feedback receiver module (112).

According to the present implementation, the platform (100) includes other modules (214). A tag module is present in other modules (214). The tag module is accessible to the customers and is configured to enable tagging and customize tagging of the music pieces. Further, the tag module is configured to generate tags based on the customer preferences. The tag module is accessible to the artists and is configured to enable artist to tag other artists related with the music pieces.

Fig. 3 illustrates a computer implemented method (300) for predicting and distributing online content in the target computing system (202). The methods (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, functions that perform particular functions or implement particular abstract data types. The methods (300) may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

The order in which the methods (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) or alternative methods. Additionally, individual blocks may be deleted from the method (300) 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 302, the method (300) includes storing in the listener repository at least listener registration information and listener profile information, wherein profile information includes at least likes and dislikes, interest related information, hobbies, permanent address, temporary address, music preferences, favourite artists, age, contact details and sex. In an implementation, the listener repository (122) is configured to store the information related to listeners. The information related to listeners includes but is not restricted to listener's profile information, registration information, listener preferences and privacy settings. At block 304, the method (300) includes storing in the artist repository at least registration information corresponding to users registered as artists and artists profile information. In an implementation, the artist repository (126) is configured store the information related to artists. The information related to artists includes but is not restricted to artist's profile information, registration information, artist's payment information and tags created by the artists.

At block 306, the method (300) includes storing in the advertisement repository a plurality of advertisements, wherein the plurality of advertisements are stored in a format selected from at least a text format, an image format, an audio format, a video format and a combination format. In an implementation, the advertisement repository (124) is configured to store the information related to the advertisers and advertisements. The advertisements are stored in a format but are not restricted to a text format, an image format, an audio format, a video format and combination format.

At block 308, the method (300) includes extracting the listener profile information from the listener repository. In an implementation, the extractor module (120) is configured to extract the listener profile information from the listener repository (122).

At block 310, the method (300) includes determining the listener profile information from said extracted listener profile information for each listener, wherein the listener profile information comprises at least likes and dislikes, preferences, sex, age, hobbies, music preferences, favourite artists and contact details. In an implementation, the determinator module (104) is configured to determine the listener profile information such as but not restricted to likes and dislikes, preferences, sex, hobbies, reviews posted, rating pattern, contact details from the extracted listener related information for each listener.

At block 312, the method (300) includes categorizing the advertisements present in the advertising repository based on a unique predictive methodology in a real time. In an implementation, the categorization module (118) is configured to categorize the advertisements based on a unique predictive methodology in real time.

At block 314, the method (300) includes mapping the listener profile information related to the listener with the categorized advertisements. In an implementation, the mapping module (106) is configured to map the determined information related to the listener with the categorized advertisements. At block 316, the method (300) includes aggregating the mapped advertisements corresponding to each listener. In an implementation, the aggregator module (116) is configured to aggregate all the mapped advertisements corresponding to each listener.

Further, at block 318, the method (300) includes pushing the aggregated advertisements for each listener to the corresponding listener's contact details. In an implementation, the pushing module (114) configured to receive the aggregated information for each listener and pushes the all aggregated advertisements to the corresponding listener's contact details.

Although implementations for predicting and distributing online content have been described in language specific to structural features and/or method, it is to be understood that the appended claims are not necessarily limited to the specific features or method described. Rather, the specific features and method are disclosed as exemplary implementations for predicting and distributing online content.

TECHNICAL ADVANCEMENTS: The technical advantages of the system and method for predicting and distributing online content envisaged by the present disclosure include the following:

• a system that predicts a user's likes and dislikes based on the user's preferences;

• a system that pushes advertisement related information to the users based on the prediction of the user's like and dislike; and

• a system that pushes advertisement related information based on the user's Global Positioning System (GPS) location.

While considerable emphasis has been placed herein on the particular features of this invention, it will be appreciated that various modifications can be made, and that many changes can be made in the preferred embodiment without departing from the principles of the invention. These and other modifications in the nature of the invention or the preferred embodiments will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.

Claims

CLAIMS:

1. A computer implemented method (300) for predicting and distributing an online content on an online music platform, the method comprising:

storing (302), in a listener repository (122), at least listener registration information and listener profile information;

storing (304), in an artist repository (126), at least registration information corresponding to users registered as an artist and artists profile information;

storing (306), in an advertisement repository (124), a plurality of advertisements, wherein the plurality of advertisements are stored in a format selected from at least a text format, an image format, an audio format, a video format and a combination format;

extracting (308) the listener profile information from the listener repository (122); determining (310) the listener profile information from said extracted listener profile information for each listener;

categorizing (312) the plurality of advertisements present in the advertising repository (124) based on a predictive attributes in a real time;

mapping (314) the determined listener profile information with the categorized advertisements; aggregating (316) the mapped advertisements corresponding to each listener; and pushing (318) the aggregated advertisements for each listener to the corresponding listener's contact details.

2. The method as claimed in claim 1, wherein said method further includes receiving feedback information related to behavioral attributes of the plurality of listeners at regular time intervals from said plurality of listeners.

3. The method as claimed in claim 1 , wherein said method further includes refining the mapping process with said feedback information received from the plurality of listeners.

4. The method as claimed in claim 1 , wherein said method further includes determining location of the listener using the Global Positioning System (GPS).

5. The method as claimed in claim 4, wherein said method further includes determining the appropriate listener profile information based on the GPS location of the listener.

6. The method as claimed in claim 5, wherein said method further includes mapping the categorized advertisements with the determined listener profile information for each of the listener based on the GPS location of the listener.

7. A computer implemented platform (100) for predicting and distributing an online content, wherein said platform is based on a non-transitory medium and accessible via computer network, said platform comprising:

a listener repository (122) configured to store at least listener registration information and listener profile information;

an artist repository (126) configured to store at least registration information corresponding to users registered as artists and artists profile information;

an advertisement repository (124) configured to store a plurality of advertisements in a format selected from at least a text format, an image format, an audio format, a video format and a combination format;

an extractor module (120) cooperating with said listener repository, said extractor module configured to extract the listener profile information stored in the listener repository; a determinator module (104) cooperating with said extractor module, said determinator module configured to receive the extracted information from the extractor and determines the listener profile information;

a categorization module (118) cooperating with said advertisement repository, said categorization module configured to access said advertising repository and categorizes the advertisements based on a predictive attributes in a real time;

a mapping module (106) cooperating with said categorization module and determinator module, said mapping module configured to receive the listener profile information from the determinator module and maps said information with the categorized advertisements;

an aggregator module (116) cooperating with said mapping module, said aggregator module configured to aggregate all the mapped advertisements corresponding to each listener; and

a pushing module (114) cooperating with said aggregator module, said pushing module configured to receive the aggregated advertisements for each listener and pushes all said aggregated advertisements to the corresponding listener's contact details.

8. The platform as claimed in claim 7, wherein said platform further comprises a feedback receiver module (112) accessible to the listeners and configured to receive feedback information related to behavioral attributes of the plurality of listeners at regular time intervals from said plurality of listeners.

9. The platform as claimed in claim 7, wherein said platform further comprises a refinement tuner module (110), said refinement tuner module cooperates with the mapping module.

10. The platform as claimed in claim 9, wherein said refinement tuner module checks the mapping of the categorized advertisements with listener profile information and further refines the mapping process with the feedback information received from the feedback receiver module.

11. The platform as claimed in claim 7, wherein said platform further comprises a location module (108), said location module (108) cooperates with the determinator module (104) and mapping module (106).

12. The platform as claimed in claim 11, wherein said location module (108) receives location based information of the listener using the Global Positioning System (GPS).

13. The platform as claimed in claim 12, wherein the determinator module (104) determines the appropriate listener profile information based on the GPS location of the listener.

14. The platform as claimed in claim 13, wherein the mapping module (106) maps the categorized advertisements with the determined listener profile information corresponding to each listener based on the GPS location of the listener.