System and method for providing a smart content experience on a digital platform

Abstract

A system and method for providing a smart content experience on a digital platform, the method comprising, receiving, by a transceiver unit, a request to view a media content from a first user, wherein the first user is associated with a first user profile. Thereafter, the method further comprising, identifying, by a processing unit, a target category from a set of prestored categories based on an analysis of the first user profile, wherein the target category is associated with an interaction dataset, the interaction dataset being associated with the media content, and then applying, by the processing unit, one or more target actions on the media content based on the identification of the target category.

Description

SYSTEM AND METHOD FOR PRO VIDING A SMART CONTENT EXPERIENCE ON A DIGITAL PLATFORMTECHNICAL FIELD

[0001] The present invention relates to content provisioning on a digital platform. More specifically, the present invention pertains to the field of providing a smart content experience on a digital platform by enhancing the user-interaction with media content.BACKGROUND

[0002] With the availability of wider access to the Internet and digital services across the globe, there has been a constant rise in the number of users accessing digital platforms. More specifically, this ease of accessibility has facilitated a rise in the demand for various types of media content available on digital platforms, such as movies, videos, blogs, books, podcasts etc.

[0003] Naturally, the increase in the number of users and the demand for media content is also accompanied by an ever-increasing number of user preferences with respect to the media content. Most digital platforms rely on enhancing the user experience by providing recommendations of media content that a user is likely to watch or read. For instance, if the user preferences indicate that the user is a male user who watched football related content on the digital platform, the existing recommendation systems recommend sports related content to this user on the platform. However, with the growth of technologies related to digital platforms, the preferences of the modern user are not merely restricted to the type of media content to be consumed, but instead, the user preferences have evolved to include the manner in which the user is able to view and interact with the media content.

[0004] Furthermore, a large number of digital platforms rely on ad-revenue to operate. Therefore, it is important for digital platforms to maximize user engagement with the ads while maintaining a content-centric environment where the manner or frequency of ads do not diminish the user experience.

[0005] Therefore, in light of the above, there is a need in the art to provide a smart content experience on a digital platform that enhances the user experience with respect to not only whatcontent is recommended to the user but also improves the manner in which the user is able to view, navigate and interact with the recommended content.OBJECTIVES

[0006] This section is provided to introduce certain objectives and aspects of the present invention in a simplified format, that are then further elaborated upon in the subsequent paragraphs provided in the section of detailed description of the present disclosure.

[0007] In order to overcome at least a few of the problems of the known solutions as provided in the previous section, an objective of the present disclosure is to substantially reduce the limitations and / or drawbacks of the prior arts as described herein above.

[0008] An objective of the present disclosure is to provide a solution that enables a smart content experience on a digital platform.

[0009] Another objective of the present disclosure is to provide a solution that reduces the resources required by a digital platform, while increasing the cost-effectiveness of operations.

[0010] Another objective of the present disclosure is to provide a solution that automatically customizes user interaction with a media content based on the interactions of other users sharing similar characteristics.

[0011] Another objective of the present disclosure is to provide a solution that enables assisted navigation in a content.

[0012] Another object of the present disclosure is to provide a solution that reduces the need for the performance of repetitive actions by the users to navigate a content.

[0013] Another objective of the present disclosure is to provide a low-overhead solution for converting an unindexed content into a smart content by using user interactions.

[0014] Another objective of the present disclosure is to provide a solution reduce time-related and computational costs associated with navigation of unindexed content.

[0015] Yet another objective of the present disclosure is to provide a solution that enables efficient deployment of ads to minimise disruption of user experience, while maximizing user engagement.SUMMARY

[0016] An aspect of the present invention relates to a method of providing smart content experience on a digital platform. The method comprises receiving, by a transceiver unit, a request to view a media content from a first user, wherein the first user is associated with a first user profile. The method further comprises identifying, by a processing unit, a target category from a set of prestored categories based on an analysis of the first user profile, wherein the target category is associated with an interaction dataset, the interaction dataset being associated with the media content. Thereafter, the method further comprises, applying, by the processing unit, one or more target actions on the media content based on the identification of the target category.

[0017] In an exemplary implementation, the method further comprises that the one or more target actions are performed dynamically during the playback of the media content.

[0018] In an exemplary implementation, the method further comprises that the application of the one or more target actions is performed at one or more timestamps of the media content associated with the one or more target actions.

[0019] In an exemplary implementation, the method further comprises, generating the set of prestored categories based on the basis of retrieving, by the transceiver unit, a set of interactions from a set of users associated with the digital platform, wherein the set of interactions is associated with the media content. Thereafter, identifying, by the processing unit, one or more patterns associated with the media content based on the set of interactions. Thereafter, generating, by the processing unit, a set of categories based on at least one of the one or more patterns and a user profile associated with each user from the set of users, and then storing, by the processing unit, the generated set of categories in a storage unit.

[0020] In an exemplary implementation, the method further comprises that each user profile comprises a set of first attributes of the user, wherein the set of first attributes comprises at least a personal attribute, and at least a preference attribute.

[0021] In an exemplary implementation, the method further comprises, that the application of the one or more target actions on the media content may be performed automatically.

[0022] In an exemplary implementation, the method further comprises providing, a notification for applying the one or more target actions, and then receiving, a user input in response to the notification, prior to the application of the one or more target actions on the media content.

[0023] In an exemplary implementation, the method may further comprise that the user input may comprise at least one of positive user input and negative user input.

[0024] In an exemplary implementation, the method, after receiving the user inputs, may further comprise, applying, the one or more target actions on the media content in the event of receiving a positive user input, and dismissing, the notification for applying the one or more target actions in the event of a negative user input.

[0025] In an exemplary implementation, the method may further comprise that the consumption of the media content for each user from the set of users is greater than a pre-defined threshold.

[0026] Another aspect of the present disclosure may relate to a system for providing smart content experience on a digital platform. The system comprises a transceiver unit configured to receive, a request to view a media content from a first user, wherein the first user is associated with a first user profile. The system further comprises a processing unit configured to identify, a target category from a set of prestored categories based on an analysis of the first user profile, wherein the target category is associated with an interaction dataset, the interaction dataset being associated with the media content. Further, the processing unit of the system may apply, one or more target actions on the media content based on the identification of the target category.BRIEF DESCRIPTION OF DRAWINGS

[0027] FIG. 1 illustrates an exemplary computing environment for providing a smart content experience, in accordance with an exemplary implementation of the present disclosure.

[0028] FIG. 2 illustrates an exemplary system for providing a smart content experience, in accordance with an exemplary implementation of the present disclosure.

[0029] FIG. 3 illustrates an exemplary block diagram wherein the steps to provide a smart content experience on a digital platform are shown, in accordance with an exemplary implementation of the present disclosure.

[0030] FIG. 4 illustrates an exemplary method flow diagram for providing a smart content experience, in accordance with an exemplary implementation of the present disclosure.

[0031] FIG. 5 illustrates another exemplary method flow diagram for providing a smart content experience, in accordance with an exemplary implementation of the present disclosure.DETAILED DESCRIPTION

[0032] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.

[0033] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0034] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.

[0035] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of theoperations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.

[0036] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0037] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.All modules, units, components used herein, unless explicitly excluded herein, may be software modules or hardware processors, the processors being a general -purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

[0038] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media,flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions. Further, as used herein, storage unit may refer to a single or a plurality of units. Further, the storage unit may be implemented in the form of a local storage, or a cloud storage, or both. Alternatively, the storage unit may be implemented in one or more servers associated with a content distribution network (CDN), wherein the storage unit may be used as a temporary cache for a set of data.

[0039] As used herein the transceiver unit may include at least one receiver and at least one transmitter configured respectively for receiving and transmitting data, signals, information or a combination thereof between units / components within the system and / or connected with the system. The transceiver unit may further comprise one or more coding / encoding components, relay etc., any or all of which may work individually or in combination with one or more components of the transceiver unit to perform the functions of transmitting and receiving data, signals, information etc.

[0040] FIG. 1 illustrates an exemplary computing environment

[0100] for providing a smart content experience on a digital platform, in accordance with an exemplary implementation of the present disclosure. As depicted in FIG. 1, the computing environment

[0100] includes a set of users

[0102] , at least one centralized server

[0104] , a network

[0106] and a first user

[0108] ,

[0041] As further depicted in FIG.l, each of the set of users

[0102] , the centralized server

[0104] and the first user

[0108] may be connected to the network

[0106] , As used herein, the network

[0106] may be one of a wide area network (WAN), e.g. internet, or a local area network (LAN). Further, the components / units, as depicted, may be connected to the network

[0106] using at least one of a wired connection and a wireless connection, or a combination thereof. A wired connection may comprise an ethernet connection, an optical fibre connection, or any other wired network connection as may be known to a person ordinarily skilled in the art. Further, a wireless connection may comprise a connection based on at least one of the following wireless communication technologies, such as Wi-Fi, Li-Fi, Radio Frequencies, Infrared, Cellular Communication (3G, 4G, 5G), Bluetooth, Near Field Communication (NFC), satellite communication and / or any other wireless communication technology as may be known to a person ordinarily skilled in the art.

[0042] As used herein, the centralised server

[0104] may comprise one or more computing devices comprising the back-end of a digital platform, wherein, the one or more computing devices,individually, or collectively may be responsible for providing one or more services associated with the digital platform, to one or more user devices. It is to be appreciated that the term “server,” as used herein, may be used to denote an individual server, or a group of servers, or a distributed server, all or any of which may be local servers, or remote servers, or a combination thereof.

[0043] As used herein, the set of users

[0102] may comprise a set of users who have previously consumed a media content that is requested by the first user

[0108] ,

[0044] As used herein, the first user

[0108] may refer to a user who requests a media content, on which one or more target actions may be performed to provide a smart content experience on the digital platform. It may be understood that the first user

[0108] may request for a media content using a user device, wherein a user device may include, but is not limited to, a smartphone, a tablet, a laptop, a desktop computer, a media player, a console, an AR / VR headset, a television, and a smart projector. It may be further understood that the first user

[0108] may have a first profile associated with the first user

[0108] , and therefore a different user device, each having access to the first profile, may be used to request a media content at different instances of time. Further, it may be understood that hereinafter, any reference to the first user

[0108] performing an action may be construed to mean that the first user

[0108] may perform the action via a user device.

[0045] In FIG. 1, an exemplary system for providing a smart content experience to a digital platform (not shown) is implemented at the centralized server

[0104] , However, it may be understood by a person skilled in the art that the aforementioned implementation is not intended to limit the scope of the present disclosure, and that the exemplary system for providing a smart content experience on a digital platform may also be implemented outside of the centralised server, for example, at a user device, or any other device as may be known to a person ordinarily skilled in the art.

[0046] In operation, the set of users

[0102] may consume a media content provided by a digital platform, wherein, each interaction of the set of users

[0102] with the media content, e.g., pausing, replaying, skipping etc. may be stored in an interaction dataset along with an information corresponding to a user profile of each user, on the centralised server

[0104] , Thereafter, the exemplary system implemented on the centralised server

[0104] may process one or more sets of interactions to generate and store a set of categories associated with the interaction dataset. Thereafter, when the first user

[0108] sends a request to view a media content, upon receipt of the request, the centralised server

[0104] analyses a profile associated with the first user

[0108] , identifiesa target category from the set of pre-stored categories, and thereafter applies one or more target actions to the media content requested by the first user

[0108] to provide a smart content experience on the digital platform. In an implementation, the application of the one or more target actions to the requested media content may be applied dynamically during the period of user playback of the media content. The detailed working of the embodiments of the present disclosure shall be more apparent from the description of the below figures.

[0047] FIG. 2 illustrates an exemplary implementation of the present disclosure, wherein a system

[0200] for providing a smart content experience on a digital platform is shown. As depicted in FIG. 2, the system

[0200] may include at least one transceiver unit

[0202] , at least one processing unit

[0204] , at least one storage unit

[0206] and other unit(s)

[0208] , It may be noted that the other unit

[0208] may perform any function that may be ancillary to the transceiver unit

[0202] and / or the processing unit

[0204] to provide a smart content experience on a digital platform.

[0048] It may be understood by a person ordinarily skilled in the art that the aforementioned units of the system

[0200] , as depicted in FIG. 2, are merely exemplary, and in no manner to be construed to limit the scope of the present subject matter in any manner. The system

[0200] may include other units, or modules, or engines, or components known to a person skilled in the art, which may be in communication with the exemplary units, as depicted in FIG. 2, of the system

[0200] , Such components have not been depicted in FIG. 2 and explained here for the sake of brevity. It may be further noted that the system

[0200] may be implemented using, or without any or all of the above-mentioned components and / or in with the addition of any other component(s).

[0049] Further, the system

[0200] is configured to provide a smart content experience on a digital platform with the interconnected working of one or more of its components / units. To provide the smart content experience, the system

[0200] may be implemented in a plurality of configurations, in accordance with various implementations of the present disclosure. For example, the system

[0200] may be implemented as an integrated component inside a server associated with a digital platform, such as the centralized server

[0104] depicted in FIG.l, wherein the integration of system

[0200] enables the digital platform provider to provide a smart content experience on their platform without impacting the server operation costs or load capacity. In another example, the system

[0200] may be implemented as a plug-in component on existing / legacy hardware for servers associated with digital platforms, thereby allowing them to provide a smart content experience without a need to upgrade and / or replace the existing hardware. Similarly, in another example, the digital platform may be configured to communicate with one or more remote servers and / or CDNs on which thesystem

[0200] may be implemented, thereby allowing the features of the present disclosure to implemented on a demand basis. In yet another example, the system

[0200] may be implemented as a set of components spread across multiple devices, wherein each of the devices are working in an interconnected manner to facilitate the joint working of each of the components of system

[0200] , As such, it may be understood by a person ordinarily skilled in the art that the aforementioned list of examples is not intended to limit the scope of the present disclosure and the system

[0200] may be implemented in a plurality of alternative configurations and on a plurality of devices.

[0050] In operation, a first user

[0108] , who is associated with a corresponding first user profile, may send a request to a digital platform for viewing a media content. Thereafter, the transceiver unit

[0202] may receive the request to view a media content from the first user

[0108] ,

[0051] In an exemplary implementation, the first user

[0108] may be a target user to whom the smart content experience on the digital platform may be provided. Further, the first user profile associated with the first user may comprise a set of first attributes associated with the user. The set of first attributes may comprise one or more of user preferences and one or more user attribute information, e.g., age or gender, user location. Thereafter, the transceiver unit

[0202] may transmit the request to the processing unit

[0204] ,

[0052] Continuing with the operation of the system

[0200] , the processing unit

[0204] may process a set of data, in response to the request received, to enable the provision of a smart content experience to the first user

[0108] , More specifically, in operation, a target category from among a set of prestored categories may be identified by the processing unit

[0204] , Further, the target category identified by the processing unit

[0204] may be a category associated with an interaction dataset that corresponds to the media content requested by the first user

[0108] ,

[0053] In an exemplary implementation, the interaction dataset may include a dataset comprising one or more sets of interactions associated with a media content. Here, the one or more sets of interactions within the dataset may represent a raw interaction data, i.e., non-categorized and / or non- segregated data. For the purpose of clarity, the raw interaction data may be understood as a collection of different interactions in no particular order except the order that the interactions may be collected in. In another exemplary implementation, the interaction dataset may comprise raw interaction data pool for different attributes.

[0054] In another exemplary implementation, the interaction dataset may be built on the basis of a periodic interaction reporting by the user device of each user from the set of users

[0102] , The system

[0200] may retrieve the set of interactions performed by the user of the device, at pre-defined periodic intervals. In another implementation, the periodically retrieved set of interactions may be combined with other other playback or device related information, such as playback stats, device capability etc. The set of interactions retrieved herein may include each and every interaction of the user on the digital platform, including set of interactions outside of content playback, such as search history or playback settings / preferences. In yet another implementation, the set of interactions retrieved from the user device by the system

[0200] may be associated with the corresponding timestamps for each of the interactions.

[0055] In yet another implementation, the set of interactions may be converted into data packets and retrieved via an ingestion service implemented at the system

[0200] , The set of interactions may thereafter be added to the interaction dataset. As used herein, the interaction dataset may create a new entry for each interaction from the set of interactions retrieved.

[0056] In another exemplary implementation, the set of pre-stored categories may be generated and stored by the system

[0200] , To generate the set of pre-stored categories, the transceiver unit

[0202] may retrieve a set of interactions from the set of users

[0102] on the digital platform, wherein the retrieved set of interactions may be associated with the media content, e.g., replaying a specific scene from a media content, skipping a portion of the media content, selection of a dubbed audio in a preferred language etc. It may be noted that the set of interactions from the interaction dataset having a higher rate of recurrence may be prioritized over sets of interactions with a lower rate of recurrence.

[0057] Continuing with the exemplary implementation, after the set of interactions have been retrieved, the processing unit

[0204] may then identify, on the basis of the set of interactions, one or more patterns that may be associated with the media content. The identification of the one or more patterns may be achieved by processing the set of interactions in the raw data format, and classifying them into one or more recurring patterns, e.g., a pattern of skipping a specific song in the media content by a plurality of users within the set of users. Thereafter, the processing unit

[0204] may generate a set of categories on the basis of one or more patterns identified and a user profile that is associated with each user within the set of users.For example, the processing unit

[0204] may take an observed recurring pattern from the set of interactions, such as skipping a specific portion of the media content. Thereafter, the processingunit

[0204] may further segregate the data of the observed pattern on the basis of the user profiles from which the set of interactions within the observed pattern have been obtained. For illustration purposes, the processing unit

[0204] may generate a category relating to an age bucket, e.g., 21-30 or 31-40. Alternatively, a category may be generated on the basis of locations associated with the user profiles, e.g., users from city X. It may be understood by a person ordinarily skilled in the art that the aforementioned approaches for generating a category are merely for the purpose of illustration and not intended to limit the scope of the present disclosure in any manner.

[0058] Further, the processing unit

[0204] may generate a set of categories, which may further comprise a plurality of categories, each of which has been generated on the basis of an association between one or more patterns and user profiles, each associated with a user (from the set of users), wherein each profile is further associated with the one or more patterns. Thereafter, the set of categories may be stored by the processing unit

[0204] , in the storage unit

[0206] , from where it may be retrieved, as required, to implement one or more features of the present invention. For the purpose of this disclosure, the set of categories that are stored in the storage unit

[0206] may be referred to as the set of pre-stored categories during the implementation of one or more features of the present disclosure. Further, the set of pre-stored categories may be stored dynamically, meaning thereby, that the pre-stored categories may be updated periodically. In another implementation, the pre-stored categories may be updated when a threshold limit of time has elapsed. In yet another implementation, the pre-stored categories may be updated in response to accumulation of a threshold of new set of interactions associated with the requested media content are retrieved by the system

[0200] . It may be understood that the dynamic updating of the set of pre-stored categories may comprise adding new categories, removing existing categories or modifying the existing categories.

[0059] It may be understood that the set of users

[0102] on the digital platform may refer to a set of users who have consumed at least an amount of the requested media content that is greater than a predefined threshold. For example, a digital platform may set that only the users who have consumed over 70% of the content may be added to the set of users, and therefore, the set of interactions may be obtained only for the users with a consumption amount greater than 70% of the media content. Further, the set of users may be dynamic set, meaning thereby, that the set of users may change over time, wherein new users may be added to the set, one or more of the existing users of within the set may be removed, or a combination thereof. As described herein, the parameter for the addition or removal of users from the set of users may be pre-defined by the digital platform.

[0060] It may be further understood by a person ordinarily skilled in the art that the plurality of categories within the set of categories, need not be associated with only a single pattern observed from within the set of interactions. The present disclosure also encompasses, that the plurality of categories may be generated from different observed patterns, and then stored into the set of categories. For example, a set of categories may contain categories from age 21-30 for different observed patterns associated with the media content, such as pausing at a specific portion of the content, scrolling back to a previous portion of the content from another portion of the content etc.

[0061] In yet another exemplary implementation, the target category obtained from the set of prestored categories may be selected based upon the set of first attributes of the user stored in the user profile. It may be understood by a person ordinarily skilled in the art that the use of the term ‘target category,’ in the context of this disclosure is for the sake of ease of understanding, and in no manner intended to limit the scope of the present disclosure. As may be further understood, the scope of the term encompasses the identification of one or more target categories.

[0062] In yet another implementation, the system

[0200] may not obtain a target category from the set of pre-stored categories first user

[0108] , Instead, the first user

[0108] may be presented with a plurality of categories from the pre-stored categories, wherein the first user

[0108] may be presented with an option to manually select the one or more target categories that may be the most relevant to the first user

[0108] , In another implementation, the plurality of categories may comprise all categories from the pre-stored set of categories.

[0063] Returning to the operations of the system

[0200] , after the identification of the target category from the set of pre-stored categories, the processing unit

[0204] may apply one or more target actions on the media content on the basis of the target category. It may be noted that as used herein, the one or more target actions may refer to any action that may be applied to a media content or any other element associated with the viewing experience thereof, e.g., ads, either automatically or at the option of the user. In an exemplary implementation, a target action may refer to an interaction corresponding to an observed pattern that is associated with a target category. Additionally, in one implementation, when a target category and associated one or more target actions for a first user

[0108] are determined, the same may be stored by the system

[0200] in a nearest cache server. The cache server may be a part of a content distribution network (CDN). As a result, wherein the same one or more target actions are determined for another first user

[0108] ,the one or more target actions may be downloaded as meta data from the nearest cache server instead of the main server.

[0064] Further, in another exemplary implementation, the application of one or more target actions may refer to applying one or more target actions to the viewing experience of the requested media content for the first user. In an implementation, the one or more target actions may be downloaded to the user device associated with the first user

[0108] along with the requested media content. Herein, the one or more target actions may be downloaded in the form of associated meta data. Alternatively, wherein the requested media content is being streamed by the user, the one or more target actions may be downloaded and applied in real time. In another implementation, wherein the requested media content is streamed by the user, the one or more target actions for the requested media content may be downloaded in entirety (as metadata) at the start of the content playback.

[0065] Thereafter, upon the initiation of playback of the requested media content, the digital platform, or any associated application or playback medium thereof, may retrieve the meta data of one or more target actions and apply the target actions at the corresponding time stamps.

[0066] In yet another implementation, the one or more target actions downloaded on the device may be applied prior to the initiation of the content playback. For example, wherein the first user

[0108] searches for a content on the digital platform, a toggle for initiating the playback of the requested media content, such as a play button, may be displayed. Alternatively, the content may be played from a list of contents by selecting a thumbnail or configured user interface element. Here, the playback toggle or the configured user interface element may be pre-customized with the one or more target actions for the first user

[0108] , In another example, the playback toggle or the configured user interface element may be customized using the one or more target actions to start the playback of the content after the skipping of the title page, or from a fixed time stamp. Similarly, the notification for the one or more target actions may be implemented directly into the playback toggle or the configured user interface element, thereby prompting the user to select an input for the implementation of the one or more target actions before the playback of the content.

[0067] In yet another exemplary implementation, wherein the first user

[0108] was presented with an option to manually select one or more target categories from the plurality of categories, the one or more target actions applied to the requested content may correspond to the specific target categories selected by the first user

[0108] ,

[0068] It may be understood by a person ordinarily skilled in the art that the aforementioned examples are merely to illustrate one of the many implementations wherein the one or more target actions may be performed prior to the playback of content. As such, the examples shall not be construed to limit the scope of the present disclosure. It may be further understood that the present disclosure may be implemented by any other combination of one or more target actions prior to the playback of the content.

[0069] For ease of understanding, considering an example wherein a first user X sends a request to view a media content ABC. The system

[0200] may receive the request from the first user X, via the transceiver unit

[0202] , Thereafter, the system

[0200] , via the processing unit

[0204] refers to a pre-stored set of categories that have been generated on the basis of previous interactions of a set of users with the requested media content. From these set of pre-stored categories, the processing unit

[0204] may identify a target category by analysing the first user profile associated with the first user X and matching it with corresponding categories within the set of pre-stored categories. For illustration, the processing unit

[0204] may see from the first user profile of the first user X that the age of the first user is 24, the first user X belongs to a city G, and that the first user X prefers content related to a certain character E.

[0070] Thereafter, the processing unit

[0204] may try to locate categories from the set of pre-stored categories that may correspond to one or more patterns associated with groups of individuals aged 21-30, categories corresponding to one or more patterns associated with groups of individuals located in the city G, and categories corresponding to one or more patterns associated with groups of individuals who interacted, e.g., replayed, with the media content upon seeing a cameo of character E. Any or all of these categories may be combined to form a set of target categories.

[0071] Now, the processing unit

[0204] will apply the interactions of previous users that are associated with the observed patterns of the target categories, to the viewing experience of the first user X. Meaning thereby, that when the first user X views the content ABC, the system

[0200] may automatically skip a song at 2 hours 23 minutes on the basis of the interactions by a plurality of users who previously viewed the content and are in the age bracket of 21-30. Similarly, the system

[0200] may automatically replay a popular cameo scene of the character E within the requested media content.

[0072] In another exemplary implementation, the processing unit

[0204] may generate at least one of the pre-stored categories on the basis of identification of one or more ad engagement patterns.As used herein, the one or more ad engagement patterns may be based on the interactions of the set of users with one or more ads while consuming the media content. Such ad engagement patterns may include, but are not limited to, the duration of one or more ads watched by a user before skipping the ad, the number of ads watched by a user before the user decided to pause the consumption of content, and the categories of ads which received the highest engagement during viewing experience of a requested media content. Additionally, ad engagement patterns may also include a tolerance rate for a set of users

[0102] , wherein the tolerance rate may be associated with the user tolerance against the replaying of the same ad at different timestamps across the playback duration of the requested media content. Alternatively, the tolerance rate may be based upon user tolerance against a set of ads that may be displayed across the playback duration of the requested media content. Thereafter, one or more categories generated on the basis of ad engagement patterns may be included in the target categories to provide a less disruptive and more engaging ad experience to the first user while viewing the requested media content. In operation, the system

[0200] may enable the digital platform to effectively determine one or more parameters related to ad engagement, wherein these may be used to identify a set of preferred timestamps for ad placement. The system

[0200] may also be able to gauge the optimum length and types of ads that may be best suited for ad engagement, on the basis of the attributes of the set of users and the attributes of the requested media content.

[0073] As a result, the invention of the present disclosure enables the learning of user patterns to not only enhance the experience of watching a requested content on a digital platform, but it also maximizes the earning potential for a digital platform by guiding effective deployment of ads for maximum engagement.

[0074] In another exemplary implementation, the one or more target action may comprise altering the resolution of the media content, by the processing unit

[0204] , This may be implemented in scenarios wherein the media content is a video-based content with a variable bitrate, and certain portions of the media content, e.g., a scene depicting a high level of activity and colour variation, are having a higher bitrate in contrast to the other scenes of the media content. Therefore, in certain scenarios the network bandwidth associated with users in a specific area may not be sufficient to support high-bitrate portions of the media content, thereby requiring the users to manually lower the resolution to continue watching the media content. In some scenarios, even when the change in resolution is performed automatically, the available solutions are unable to avoid the associated buffering of the content. Such interactions of the user with the media content, or the automatic / pre- configured changes may also be stored in the interaction dataset from which one or more patternsmay be identified by the processing unit

[0204] , Thereafter, these set of interactions may be converted into one or more target actions for a first user

[0108] , wherein the first user

[0108] encounters an event of high resource utilization due to variable bit rate associated with the requested media content. As a result, the system

[0200] may be able to automatically change the resolution of the content, without any input by the first user

[0108] , This ensures a smooth playback for the first user

[0108] ,

[0075] For the ease of understanding, let us take an example wherein the system

[0200] may observe a pattern from the interaction dataset that at the duration of 1 hour and 25 minutes, a plurality of users are lowering the resolution of the media content. Thereafter, the system

[0200] may identify a pattern from the aforementioned set of interactions that the majority of users from the plurality of users who have lowered the resolution are located in the city G. Thereafter, the system

[0200] may create a category and add it to the set of categories which corresponds to the observed pattern of lowering resolution of the media content and the user profiles of users from the city G. In response, when a first user X, who is located in the city G, views the media content, the system

[0200] may automatically identify the target category to include the category associated with the pattern of lowering resolution of the media content. Thereafter, when the first X user reaches the duration of 1 hour and 25 minutes, the system

[0200] may automatically lower the resolution for the first user X to ensure a seamless experience without buffering.

[0076] In yet another exemplary implementation, the application of the one or more target actions to the media content may be performed at the option of the first user, wherein, before the application of one or more target actions on the media content, the processing unit

[0204] may provide a notification to the first user. The notification, as used herein, may comprise a prompt which may indicate to the first user that a similar user performed an action described in the notification at the specific portion in response to a specific event related to the media content. The said notification may further comprise an option for the first user to provide one of a positive input or a negative input to the notification. In an event the first user provides a positive input, the target action suggested in the notification may be applied to the media content, and wherein the first user provides a negative input, the notification for applying the target action may be dismissed without applying the target action.

[0077] For the ease of understanding, let us take another example, wherein a user X may be desirous of watching a popular movie on a digital platform. However, the movie requested by the user X may be in a foreign language, like French. Accordingly, the user may watch the movie usingsubtitles, or alternatively, the user may utilise any available subtitles for watching the movie. In such a scenario, the system

[0200] of the present disclosure may identify one or more target actions that are derived on the basis of a target category. This target category is further based on patterns and user profiles associated with users from the set of users

[0102] that are similar to the first user

[0108] ,

[0078] Accordingly, in this example, the target action for the user X may be to change the audio playback language of the requested media content to a dubbed language alternative, wherein the dubbed language is a native and / or preferred language for other users similar to the first user

[0108] who have already watched the content. This may result in the audio playback language being changed to English. However, it may be the case that the user X may be interested in watching the requested media content in French only. Therefore, an automatic change action may not be preferrable.

[0079] Therefore, the system

[0200] may present the user X with a notification indicating the target action that may be applied, i.e., changing the audio playback language to English. Further, the notification may comprise a positive input and a negative input. Thereafter, the target action may only be applied in an event the user X chooses the positive input. Wherein the user X opts for a negative input, the system

[0200] does not apply the target action and the content is played in the current audio playback language.

[0080] As evidenced by numerous implementations of the present disclosure that have been illustrated herein above, the present disclosure enables seamless navigation for users on a digital platform and eliminates the need for the performance of repetitive actions by the users. For instance, a first user

[0108] may be associated with a target category wherein the associated users prefer to skip musical and / or dance portions of a requested media content. Here, the invention of the present disclosure provides a smart content experience by learning that other users that are similar to the first user

[0108] have repeatedly performed a skip action at the timestamps corresponding to a musical and / or dance portion of the requested media content, and therefore, it eliminates the need for the first user

[0108] to repeatedly skip the music and / or dance portion. Instead, preferred one or more target actions are automatically performed for the first user

[0108] ,

[0081] Furthermore, as may be understood, the present disclosure harnesses the data from sets of interactions that are pinged from the user devices, and intelligently segregates the data to generate a resource and time-efficient solution that converts unindexed data into smart content for the users.

[0082] Referring to FIG. 3, an exemplary block diagram comprising steps for providing a smart content experience is shown, in accordance with an exemplary implementation of the present disclosure.

[0083] At step 302, the system

[0200] receives the set of interactions from the set of users

[0102] , via the transceiver unit

[0202] , which are then transmitted to the processing unit

[0204] ,

[0084] Thereafter, the processing unit

[0204] processes the set of interactions to generate a set of categories, which are then stored in the storage unit

[0206] at step 304.

[0085] Thereafter, when a first user

[0108] requests for a media content, the request is received by the system

[0200] , via the transceiver unit

[0202] at step 306.

[0086] It may be understood that the steps 302 - 304 are not required to be performed for each request for a media content by a first user

[0108] , Instead, once the processor

[0204] has successfully generated and stored a set of categories for a media content, thereafter the same may be used to provide a smart content to a plurality of first users

[0108] on the digital platform. It may be further understood that the steps 306 - 310, as explained herein, may be performed any number of times after the generation of the pre-stored categories has concluded. Further, in an exemplary implementation, the steps 306-310 may be performed at any point of time as a independent of steps 303-304 to provide a smart content experience on a digital platform, provided that the pre-stored set of categories for the requested media content exist.

[0087] Thereafter, the request, along with a first user profile associated with the first user

[0108] is transmitted to the processing unit

[0204] , In response, at step 308, the processing unit

[0204] retrieves the pre-stored categories from the storage unit

[0206] to identify a target category from the set of pre-stored categories.

[0088] Thereafter, to provide a smart content experience on a digital platform, the processing unit

[0204] identifies one or more target actions based on the identified target category, and then, at step 310, applies the one or more target actions to the media content requested by the first user

[0108] ,

[0089] FIG. 4 illustrates an exemplary method flow diagram for providing a smart content experience on a digital platform, in accordance with an exemplary implementation of the present disclosure. As depicted in FIG. 4, the method

[0400] begins at step

[0402] ,

[0090] At step

[0404] , the method further comprises receiving a request to view a media content from a first user associated with a first user profile, via the transceiver unit

[0202] ,

[0091] In an exemplary implementation, the first user

[0108] may be a target user to whom the smart content experience on the digital platform may be provided. Further, the first user profile associated with the first user may comprise a set of first attributes associated with the user. In another exemplary implementation, the set of first attributes may comprise one or more of user preferences and one or more user attribute information, i.e., age or gender, user location.

[0092] At step

[0406] , the method further comprises identifying, using the processing unit

[0204] , a target category from a set of prestored categories based on an analysis of the first user profile. Herein, the target category is associated with an interaction dataset, the interaction dataset being associated with the media content.

[0093] In an exemplary implementation, the interaction dataset may include a dataset comprising one or more sets of interactions associated with a media content. Here, the one or more sets of interactions within the dataset may represent a raw interaction data, i.e., non-categorized and / or non- segregated data.

[0094] In another exemplary implementation, the method may further comprise, generating, and storing the set of prestored categories by the system

[0200] , The method may further comprise, retrieving, by the transceiver unit

[0202] , a set of interactions from a set of users on the digital platform, wherein the retrieved set of interactions may be associated with the media content, e.g., replaying a specific scene from a media content, skipping a portion of the media content, skipping an ad, a change in playback speed for one or more timestamps, a change in audio playback language, an addition / removal of subtitles, a change in quality of the content etc.

[0095] It may be further understood that the set of users on the digital platform (hereinafter also referred to as ‘set of users’) may refer to a set of users who have consumed at least an amount of the requested media content that is greater than a predefined threshold. For example, a digital platform may set that only the users who have consumed over 70% of the content may be added to the set of users, and therefore, the set of interactions may be obtained only for the users with a consumption amount greater than 70% of the media content. Further, the set of users may be dynamic set.

[0096] It may be further understood by a person ordinarily skilled in the art that the plurality of categories within the set of categories, need not be associated with only a single pattern observed from within the set of interactions. The present disclosure also encompasses, that the plurality of categories may be generated from different observed patterns, and then stored into the set of categories. For example, a set of categories may contain categories from age 21-30 for different observed patterns associated with the media content, such as pausing at a specific portion of the content, scrolling back to a previous portion of the content from another portion of the content etc.

[0097] In yet another exemplary implementation, the target category obtained from the set of prestored categories may be selected based upon the set of first attributes of the user stored in the user profile.

[0098] Thereafter, at step

[0408] , the method further comprises applying, using the processing unit

[0204] , one or more target actions on the media content based on the identification of the target category made at step

[0406] ,

[0099] In an exemplary implementation, a target action may refer to an interaction corresponding to an observed pattern that is associated with a target category.

[0100] Further, in another exemplary implementation, the application of one or more target actions may refer to applying one or more target actions to the viewing experience of the requested media content for the first user.

[0101] In another exemplary implementation, the method may further comprise, generating, by the processing unit

[0204] at least one of the pre-stored categories on the basis of identification of one or more ad engagement patterns. As used herein, the one or more ad engagement patterns may be based on the interactions of the set of users with one or more ads while consuming the media content.

[0102] In another exemplary implementation, the one or more target action may comprise altering the resolution of the media content, by the processing unit

[0204] ,

[0103] In yet another exemplary implementation, the method may further comprise that the application of the one or more target actions to the media content may be performed at the optionof the first user, wherein, before the applying the one or more target actions on the media content, the method may comprise providing, via the processing unit

[0204] , a notification to the first user. The notification, as used herein, may comprise a prompt which may indicate to the first user that a similar user performed an action described in the notification at the specific portion / in response to a specific event related to the media content. The said notification may further comprise an option for the first user to provide one of a positive input or a negative input to the notification. In an event the first user provides a positive input, the target action suggested in the notification may be applied to the media content, and wherein the first user provides a negative input, the notification for applying the target action may be dismissed without applying the target action.

[0104] Thereafter, at step

[0410] , the method terminates.

[0105] FIG. 5 illustrates another exemplary method flow diagram, in accordance with an exemplary implementation of the present disclosure. As depicted in FIG. 5, the method

[0500] begins at step

[0502] ,

[0106] At step

[0504] , the method comprises receiving, by the system

[0200] a set of interactions associated with a media content, from a set of users

[0102] who have consumed an amount of the media content that is greater than a predefined threshold.

[0107] At step

[0506] , the method further comprises, identifying, by the system

[0200] one or more patterns associated with the media content, wherein the identification is performed on the basis of the received set of interactions.

[0108] At step

[0508] , the method further comprises, generating, by the system

[0200] , a set of categories on the basis of at least one of the one or more identified patterns and one or more user profiles, each associated with a user from the set of users

[0102] , wherein each profile is further associated with the one or more patterns.

[0109] At step

[0510] , the method further comprises, storing the generated set of categories at the system

[0200] ,

[0110] At step

[0512] , the method further comprises receiving, at the system

[0200] a request from a first user

[0108] associated with a first user profile, to view a media content.

[0111] At step

[0514] , the method further comprises analysing, the first user profile associated with the first user.

[0112] At step

[0516] , the method further comprises, identifying, by the system

[0200] , a target category from among the set of pre-stored categories based upon the analysis of the first user profile.

[0113] At step

[0518] , the method further comprises, applying one or more target actions on the media content requested by the first user on the basis of the target category identified.

[0114] Thereafter, at step

[0520] , the method

[0500] terminates.

[0115] As is evident from the paragraphs above, the present disclosure provides a technically advanced solution for providing a smart content experience on a digital platform that is cost- efficient, highly customizable and technically advanced over the state of the art. The present solution enables a smart viewing experience for the user wherein certain actions such as skipping certain content, replaying content, etc. are automatically recommended to the user. It further ensures watch continuity by providing smart recommendations that are tailored to the user. For instance, the streaming bitrate of the content may be automatically reduced for a portion of media content for a user if other users in the same location have experienced loading issues with the media content, thereby ensuring watch continuity for this user. Further, the present solution is extremely helpful for specially abled users or challenged users or old age, as it enables the users of a digital platform to effortlessly navigate a media content without the need to perform repetitive actions. For instance, old age users may not know how to skip a song, or replay a scene, etc. due to lack of knowledge / experience with the digital platform. However, with the present solution when tailored actions are recommended to such users, it increases the overall viewing experience for such users. Furthermore, the technically advanced solution of the present disclosure enables the provision of a customized smart content experience on a digital platform dynamically, and in real-time, without the requirement for a pre-analysis of content to determine the customized target actions for a user. This significantly reduces the requirement for high-computing power that may be otherwise required by a digital platform to individually scan and index each media content for possible user actions, and then categorise it using resource-heavy and expensive models. Instead, the present disclosure enables a resource-efficient and time-friendly approach to providing a smart content experience on a digital platform.

[0116] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.

Claims

We Claim:

1. A method [400] of providing a smart content experience on a digital platform, the method [200] comprising: receiving, by a transceiver unit [202], a request to view a media content from a first user [108], wherein the first user[108] is associated with a first user profile; identifying, by a processing unit [204], a target category from a set of prestored categories based on an analysis of the first user profile, wherein the target category is associated with an interaction dataset, the interaction dataset being associated with the media content; and applying, by the processing unit [204], one or more target actions on the media content based on the identification of the target category.

2. The method [400] as claimed in claim 1 wherein the application of the one or more target actions is performed dynamically during the playback of the media content.

3. The method [400] as claimed in claim 1, wherein the application of the one or more target actions is performed at one or more timestamps of the media content associated with the one or more target actions.

4. The method [400] as claimed in claim 1 wherein the set of prestored categories are generated based on: retrieving, by the transceiver unit [202], a set of interactions from a set of users [102] associated with the digital platform, wherein the set of interactions is associated with the media content; identifying, by the processing unit [204], one or more patterns associated with the media content based on the set of interactions;generating, by the processing unit [204], a set of categories based on at least one of the one or more patterns and a user profile associated with each user from the set of users [102]; and storing, by the processing unit [204], the generated set of categories in a storage unit [206],5. The method [400] as claimed in claim 4, wherein each user profile comprises a set of first attributes of the user, wherein the set of first attributes comprises at least a personal attribute, and at least a preference attribute.

6. The method [400] as claimed in claim 1, wherein the application of the one or more target actions on the media content may be performed automatically.

7. The method [400] as claimed in claim 1, wherein prior to the application of the one or more target actions on the media content, the method comprises: providing, a notification for applying the one or more target actions; and receiving, a user input in response to the notification.

8. The method [400] as claimed in claim 7, wherein the user input may comprise at least one of positive user input and negative user input.

9. The method [400] as claimed in claim 7, wherein after receiving the user inputs, the method further comprises: applying, the one or more target actions on the media content in the event of receiving a positive user input. dismissing, the notification for applying the one or more target actions in the event of a negative user input.

10. The set of users [102] as claimed in claim 4, wherein the consumption of the media content for each user from the set of users is greater than a pre-defined threshold.

11. A system [200] for providing a smart content experience on a digital platform, the system [200] comprising: a transceiver unit [202] configured to receive, a request to view a media content from a first user, wherein the first user [108] is associated with a first user profile; a processing unit [204] configured to:■ identify, a target category from a set of prestored categories based on an analysis of the first user profile, wherein the target category is associated with an interaction dataset, the interaction dataset being associated with the media content; and■ apply, one or more target actions on the media content based on the identification of the target category.

12. The system [200] as claimed in claim 11 wherein the application of the one or more target actions is performed dynamically during the playback of the media content.

13. The system [200] as claimed in claim 11, wherein the application of the one or more target actions is performed at one or more timestamps of the media content associated with the one or more target actions.

14. The system [200] as claimed in claim 11, wherein, the transceiver unit [202] is further configured to:■ retrieve, a set of interactions from a set of users [102] associated with the digital platform, wherein the set of interactions is associated with the media content. the processing unit [204] is further configured to:■ identify, one or more patterns associated with the media content based on the set of interactions;■ generate, a set of categories based on at least one of the one or more patterns and a user profile associated with each user from the set of users [102]; andstore, the generated set of categories in a storage unit [206],15. The system [200] as claimed in claim 14, wherein each user profile comprises a set of first attributes of the user, wherein the set of first attributes comprises at least a personal attribute, and at least a preference attribute.

16. The system [200] as claimed in claim 11, wherein the application of the one or more target actions on the media content may be performed automatically.

17. The system [200] as claimed in claim 11, wherein prior to the application of the one or more target actions on the media content, the system [200] is further configured to: provide, via the processing unit [204], a notification for applying the one or more target actions; and receive, via the transceiver unit [202], a user input in response to the notification.

18. The system [200], as claimed in claim 17, wherein the user input may comprise at least one of positive user input and negative user input.

19. The system [200] as claimed in claim 17, wherein after receiving the user inputs, the processing unit [204] is further configured to: apply, the one or more target actions on the media content in the event of receiving a positive user input. dismiss, the notification for applying the one or more target actions in the event of a negative user input.

20. The set of users [102] as claimed in claim 14, wherein the consumption of the media content for each user from the set of users is greater than a pre-defined threshold.

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