Method of content rating, method of adapting content rating and content processing apparatus

Abstract

In order to provide an automated content rating and adaptation, rating affecting details in a content item are detected within a content item and content parts enclosing the detected details are defined as rated content elements. A descriptor is assigned to each rated content element and an aggregated descriptor is assigned to the content item. The descriptor includes a position of the rated content element within the content item, rating affecting factors in the rated content element, scores related to the rating affecting factors and a rating value calculated on the scores. The aggregated descriptor includes a list of the rated content elements with its rating values and an aggregated rating value of the content item calculated on the rating values of the rated content elements or on scores related to the rating affecting factors of the rated content elements.

Description

[0001] METHOD OF CONTENT RATING, METHOD OF ADAPTING CONTENT RATING AND CONTENT PROCESSING APPARATUS

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to the field of digital media technology and, more specifically, to a method of content rating, a method of adapting a content rating, a content processing apparatus configured for content marking and rating, and a content processing apparatus configured for adapting a content rating.

[0004] BACKGROUND

[0005] Typically, due to advancements in digital media technology and the internet, a large amount of digital media content, such as movies, television shows, video games, websites and the like are being published among the public. The digital media content is published for various purposes like entertainment, education, advertising, showcasing creativity, and the like. Moreover, the digital media content plays an important role in knowledge sharing, influencing as well as in personal growth amongst audiences. However, the kind of information spread through the digital media content is not always intended for positive purposes. Also, some digital media content requires an eligible audience, which has intellectual capability to distinguish between positive and negative impact of the digital media content. Hence, there is a need for categorization of the digital media content based on audiences in order to allow only eligible audiences to watch the digital media content.

[0006] Currently, certain attempts have been made to categorize the digital media content, for example by using content rating systems to classify and categorize the digital media content, such as movies, TV shows, video games, and websites, based on their suitability for different audiences. Conventionally, the categorization of the digital media content is done based on ages of the audiences, that is, age ratings are allotted based on the characteristics of audience watching the digital media content. Furthermore, the age ratings ensure that the digital media content or the entertainment content, such as movies, games, websites, and the like are clearly labelled for the minimum age group for which the entertainment content is suitable. However, the age rating is assigned on various factors, such as practices that are considered unacceptable by the majority of people, such as age-restricted videos (e.g., life-threatening acts, bestiality, necrophilia, etc.), images depicting violent acts, cruelty to animals, advertisements for addictive substances and the like. The problem associated with conventional content rating systems is that ratings provided by such systems are specified roughly per digital media content and are performed manually by trusted experts following some guidelines. In such systems, reasons for assigning appropriate age ratings are left behind the scenes and aren’t formally described. Further, the conventional content rating systems fail to support detailed content evaluation, manipulation, and enforcement to be done by content provisioning and enforcement systems. Thus, there exists a technical problem of how to reduce manual errors in digital media content evaluation as well as how to reduce the effort and complexity of the digital media content adaption curation and usage enforcement meeting that meet locality-specific requirements.

[0007] Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with conventional content rating systems.

[0008] SUMMARY

[0009] The present disclosure provides a method of content rating and a method of adapting a content rating. Moreover, the present disclosure provides a content processing apparatus configured for content marking and rating, and a content processing apparatus configured for adapting a content rating. The present disclosure provides a solution to the existing problem of how to reduce manual errors in digital media content evaluation as well as how to reduce the effort and complexity of the digital media content adaption curation and usage enforcement meeting that meet locality-specific requirements. An aim of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art and provides an improved method of content rating and an improved method of adapting a content rating as well as an improved content processing apparatus configured for content marking and rating, and another improved content processing apparatus adapting a content rating.

[0010] One or more objectives of the present disclosure are achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.

[0011] In an aspect, the present disclosure provides a method of content rating. The method comprises detecting rating affecting details in a content item and defining content parts enclosing the detected details as rated content elements. Further, the method comprises assigning to each of the rated content elements a descriptor with rating attributes. Moreover, the rating attributes comprise an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. In addition, the method comprises assigning to the content item an aggregated descriptor with aggregated rating attributes. Moreover, the aggregated rating attributes comprises a list of the rated content elements with its rating values and / or one or more other rating attributes of its descriptors, applicable rating system, and an aggregated rating value of the content item calculated on the rating values of the rated content elements or on the scores related to the rating affecting factors included into all the rated content elements within the content item.

[0012] The method includes the determination of aggregated rating value of the content item based on scores of ratings affecting factors, which is beneficial to reduce manual errors in content item evaluation as well as reduce the effort and complexity of rating the content item. The combination of aggregated rating values and the ranking manifest in the method is beneficial to provide fine details for content rating evaluation. The method includes providing an infrastructure to support smooth and efficient information evaluation of the content item as per the local regulation and consumers requirements.

[0013] In an implementation form, the method comprises embedding a rating mark into a metadata of each of the rated content elements, the rating mark comprising a rating value and / or one or more other rating attributes of a descriptor of the rated content element. Further, the method comprises embedding an aggregated rating mark into metadata of the content item, the aggregated rating mark comprising the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item.

[0014] In another implementation form, the method comprises storing a rating manifest for the content item, wherein the rating manifest comprises one or more rating attributes of the descriptor of each of the rated content elements, the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item.

[0015] In yet another implementation form, the rating value of each of the rated content elements is calculated in accordance with the applicable rating system based on the scores of the rating affecting factors included into the rated content element using weights pre-defined for different types of the rating affecting factors. Furthermore, the aggregated rating value of the content item is calculated as a function of the rating values of the rated content elements within the content item or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within the content item using the weights pre-defined for different types of the rating affecting factors.

[0016] The pre-defined weights assigned to the rating affecting factors categorize rating affecting factors based on the importance of corresponding rating affecting factors in determining content rating, which is beneficial to increase accuracy in content rating.

[0017] In an implementation form, the method further comprises repeating the steps of the method of content rating for a plurality of content items of a content block to obtain an aggregated descriptor for each of the content items and descriptors for rated content elements within each of the content items. Further, the method comprises assigning to the content block an upperlevel aggregated descriptor of a tree structure having the rated content elements as first level nodes, the content items as second level nodes and the content block as an upper-level node. Moreover, each first level node comprises a rating value and / or one or more other rating attributes of a descriptor of corresponding rated content element, each second level node comprises an aggregated rating value and / or one or more other aggregated rating attributes of an aggregated descriptor of corresponding content item. Further, the upper-level node comprises applicable rating system and an aggregated rating value of the content block calculated on the aggregated rating values of the content items of the content block or on the scores related to the rating affecting factors included into all the rated content elements within the content items of the content block.

[0018] The rating of individual rated content elements within content items, as well as aggregating ratings at different levels of the content block's structure is beneficial to provide a detailed and comprehensive understanding of the content's quality and characteristics.

[0019] In an implementation form, the method further comprises embedding an upper-level aggregated rating mark into metadata of the content block, the upper-level aggregated rating mark comprising the aggregated rating value of the content block and / or one or more other elements of the upper-level aggregated descriptor, and / or storing a rating manifest for the content block. Moreover, the rating manifest comprises the upper-level aggregated descriptor of the content block. In another implementation form, the aggregated rating value of the content block is calculated as a function of the aggregated rating values of the content items of the content block or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within each content item of the content block using weights pre-defined for different types of the rating affecting factors.

[0020] In yet another implementation form, each rating mark is based on an extendable markup language and / or have a textual or binary encoding format.

[0021] In an implementation form, the rating affecting factors comprises one or more of cultural perversions inacceptable by major audiences, pornography, indecent images of children, prohibited images of children, portraits of children in abusive context, cruelty to animals, tobacco advertisements, stirring up hatred, promotion of the criminal activities and drugs, criminal materials, raping, violent behavior, sadistic attitudes, naturally presented injuries or death.

[0022] In another aspect, the present disclosure provides a method for adapting a content rating. The method comprises obtaining rating attributes of a content item that comprises rated content elements, each rated content element enclosing a rating affecting detail, wherein the rating attributes comprise, for each rated content element, an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. Furthermore, the method comprises calculating an aggregated rating value of the content item based on the rating values of the rated content elements within the content item or on the scores related to the rating affecting factors included into all the rated content elements within the content item. In addition, the method comprises if the aggregated rating value of the content item is above a pre-defined threshold, removing and / or correcting one or more rated content elements within the content item that have top rating values or top scores related to the rating affecting factors included into said rated content elements to decrease the aggregated rating value below the pre-defined threshold.

[0023] The method includes adjusting or removing rated content items that depict content against socially acceptable norms, thereby preventing the high-rated content item to get exposed in front of an ineligible audience. Additionally, the method reduces the effort and complexity of the content item adaptation and usage enforcement meeting locality-specific requirements. Further, the method supports advanced cases, such as automated adaptation, smart content manipulation, and the like to increase accuracy in content rating and manipulation.

[0024] In yet another implementation form, the correcting of each rated content element comprises removing a rating affecting factor from said rated content element or adjusting the rating affecting factor to decrease its score.

[0025] In an implementation form, the removing or adjusting of the rating affecting factor comprises one or more of an image deletion, an image blurring, an image replacement, a sound muting, and a sound replacement.

[0026] The removing or adjusting of the rating affecting factor is beneficial to adjust or filter the content item based on the appropriate audience or locality requirements, thereby preventing exposure of inappropriate content towards an inappropriate audience.

[0027] In another aspect, the present disclosure provides a content processing apparatus configured for content marking and rating.

[0028] The content processing apparatus achieves all the advantages and technical effects of the method of content rating according to the present disclosure.

[0029] In yet another aspect, the present disclosure provides a content processing apparatus configured for adapting a content rating.

[0030] The content processing apparatus achieves all the advantages and technical effects of the method of adapting a content rating according to the present disclosure.

[0031] It is to be appreciated that all the aforementioned implementation forms can be combined. It is be noted that all devices, elements, circuitry, units, and means described in the present application could be implemented in the software or hardware elements or any kind of combination thereof. All steps which are performed by the various entities described in the present application, as well as the functionalities described to be performed by the various entities, are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity that performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements, or any kind of combination thereof. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

[0032] Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.

[0033] BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those skilled in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[0035] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[0036] FIG. 1 is a flowchart of a method of content rating, in accordance with an embodiment of the present disclosure;

[0037] FIG. 2 is a flowchart of a method of adapting a content rating, in accordance with an embodiment of the present disclosure;

[0038] FIG. 3 is a block diagram that depicts a content processing apparatus, in accordance with an embodiment of the present disclosure;

[0039] FIG. 4 is a block diagram that depicts a content processing apparatus, in accordance with a different embodiment of the present disclosure; FIG. 5 is a block diagram that depicts an exemplary scenario of a rated content element along with a descriptor in the method for content rating, in accordance with an embodiment of the present disclosure;

[0040] FIG. 6 is a block diagram that depicts an exemplary scenario of content splitting in the method of content rating, in accordance with an embodiment of the present disclosure;

[0041] FIGs. 7A, 7B, and 7C are different diagrams that depict decomposition of a content item and marking with rating values in the method of content rating, in accordance with different embodiments of the present disclosure;

[0042] FIG. 8 is a flowchart that depicts a method for content decomposition and marking with rate, in accordance with an embodiment of the present disclosure;

[0043] FIG. 9 is a diagram that depicts an exemplary scenario of defining an aggregated rating value of the content item in the method of content rating, in accordance with an embodiment of the present disclosure;

[0044] FIG. 10 is a diagram that depicts an exemplary scenario of assigning pre-defined weights to one or more rating affecting factors in the method of content rating, in accordance with an embodiment of the present disclosure;

[0045] FIG. 11 is a flowchart that depicts a method for rate adaptation, in accordance with an embodiment of the present disclosure; and

[0046] FIG. 12 is a diagram that depicts an exemplary scenario of rate adaptation in the method of adapting a content rating, in accordance with an embodiment of the present disclosure.

[0047] In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the nonunderlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing. DETAILED DESCRIPTION OF EMBODIMENTS

[0048] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practising the present disclosure are also possible.

[0049] FIG. 1 is a flowchart of a method of content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a flowchart of a method 100 of content rating. The method 100 includes steps from 102 to 108.

[0050] The method 100 is implemented to provide rating value to digital media content, which is in the form of videos, movies, TV shows, websites, and the like. The rating value is indicative of intended audience, which is eligible to watch the digital media content. The method 100 supports automated adaptation as per local requirements. The digital media content processed as per the method 100 is beneficial for content providers to expand audiences, reduce load of content preparation and prevent rate policy violations.

[0051] At step 102, the method 100 includes detecting rating affecting details in a content item. The content item refers to the digital media content, which is selected for content rating. Examples of the content item may include, but are not limited to, movies, TV shows, websites, advertisements, and the like. Further, the rating affecting details refer to elements within the content item at which the content rating is to be applied. The rating affecting details act as essential events or rate affecting events within the content item. Examples of the rating affecting details may include, but are not limited to a violent scene, an explicit image, a scene with abusive words, and the like.

[0052] At step 104, the method 100 includes defining content parts enclosing the detected details as rated content elements. The content item includes a plurality of parts, which represent a specific content. In an example, the content item is a movie. Here, the content parts refer to plurality of scenes or images from a roll film, which constitutes the movie. In an implementation, the plurality of scenes of the movie, which depicts violent acts or explicit images are defined as the rated content elements at the step 104.

[0053] At step 106, the method 100 includes assigning to each of the rated content elements a descriptor with rating attributes. The descriptor is a list of data types that disclose the information about the rated content elements. Moreover, the rating attributes include an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. For example, the content item is a movie. Here, the position of the rated content element is the timestamp associated with a definite scene. Further, the one or more rate affecting factors are expressions, which do not conform with social norms. In accordance with an embodiment, the rating affecting factors includes one or more of cultural perversions inacceptable by major audiences, pornography, indecent images of children, prohibited images of children, portraits of children in abusive context, cruelty to animals, tobacco advertisements, stirring up hatred, promotion of the criminal activities and drugs, criminal materials, raping, violent behaviour, sadistic attitudes, naturally presented injuries, or death. The rating affecting factors distinguish between socially acceptable and inacceptable expressions of art that are being presented to the audience, thereby preventing the content item to be seen by an inappropriate audience (e.g., audience below a certain age) for corresponding content item. Further, the scores related to rating factors are numerical values that define extent up to which the corresponding rating factor is unacceptable. The rating affecting factor, which is highly unacceptable is assigned score of higher magnitude as compared to the rating affecting factor, which is less unacceptable. In an example, if the rating affecting factor is a highly confidential information or a cruel murder scene, then such rating affecting factor is given higher score (as it is totally unacceptable), than the rating affecting factor, such as an advertisement of a liquor (which is less unacceptable than the murder scene). For example, the movie shows violence in bank robbery scene, and nobody killed or injured during robbery (just showing gun to force clerks to open the safe). Here, the violence shown in the scene is relatively mild and hence gets assigned a score, such as a score of 64 out of 100. Moreover, the applicable rating system refers to a rating standard, which defines rating for the corresponding content item. For example, if the content item is a video game, then pan-European game information, PEGI, is corresponding applicable rating system. In an implementation, the rating value refers to the rating provided by the applicable rating system to the content item.

[0054] At step 108, the method 100 includes assigning to the content item an aggregated descriptor with aggregated rating attributes. The aggregated descriptor is a combination of descriptors and corresponding rating attributes of all the rated content elements within the content item. For example, the content item is a movie and the rated content elements are scenes that depict violence, bad words, drug consumption, and the like. Therefore, the aggregated descriptor is a combination of descriptors of each of the scenes and the aggregated rating attributes are combinations of rating attributes of each of the scenes in the movie. Moreover, the aggregated rating attributes include a list of the rated content elements with its rating values and / or one or more other rating attributes of its descriptors, applicable rating system and an aggregated rating value of the content item calculated on the rating values of the rated content elements or on the scores related to the rating affecting factors included into all the rated content elements within the content item. For example, the method 100 includes identifying three rated content elements, such as a first scene, a second scene, and a third scene from the content item. The first scene depicts a bank robbery scene that contains use of violence, drug consumption and bad words. Here, the rate affecting factors are “violence”, “drug consumption”, and “bad words”. In such case, the descriptor of the first scene includes a scene identification number, ID, location of the first scene within the content item, size of the first scene, applicable rating system as per the guidelines of the PEGI, the rating value of the first scene as per the guidelines of the PEGI as 12 and three rating affecting factors, such as “violence”, “drug consumption”, and “bad words” with corresponding scores, such as a score of “64”, “32”, and “48” respectively. Furthermore, the combination of the scores of the rating affecting factors “violence”, “drug consumption”, and “bad words” provides the rating value of the first scene (i.e., rated content element). Similarly, the second scene and the third scene include different rating values. Thus, the list of rated content elements and corresponding rating values form a part of the aggregated rating attributes. Further, there are two rating values obtained for each rated content element - first is the rating value as per the applicable rating system, such as R1 and second is the rating value obtained by combination of the scores of the rating affecting factors, such as R2. Here, the aggregated rating value of the rated content element is determined on the basis of R1 or R2. The aggregated rating value represents overall content rating of the content item. For example, the aggregated rating value of 18 depicts that the content item is allowed to be presented to the audience above age of 18 years.

[0055] In an implementation, the aggregated rating value of the content item is calculated using appropriate guidelines and / or specific utilities implementing specific techniques, such as weighted sum, average, ranking, and the like. For example, the rating affecting factors, such as “violence”, “drugs”, and “bad Language” are assigned scores 48, 64, and 32 respectively. Therefore, the aggregated rating value or a PEGI rate is determined as PEGI = (64*6.5+32*4.2+48*7.1) / 100 = 8.17

[0056] Here, the aggregated rating value is 8+. Therefore 8+ rating is assigned to the content item.

[0057] In accordance with an embodiment, the method 100 includes embedding a rating mark into a metadata of each of the rated content elements. In an implementation, the rating mark includes an expandable list of the rating attributes, which is required for precise content rating and evaluation of the content item. The metadata of the rated content element refers to additional information associated with corresponding rated content element. The rating mark includes a rating value and / or one or more other rating attributes of a descriptor of the rated content element. The rating value is a numerical value associated with the content rating of the rated content element. In an example, the rating mark includes location, size, rating value as per applicable rating system and rating affecting factors along with corresponding scores. Further, the method 100 includes embedding an aggregated rating mark into the metadata of the content item. The aggregated rating mark includes the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item. The aggregated rating mark is combination of rating marks of each rated content element within the content item. The aggregated rating mark is beneficial for quick understanding of the content rating of the entire content item without having to examine each individual rated content element separately. Therefore, the aggregated rating attributes and aggregated rating values are indicative of whether the content item is suitable for a particular audience.

[0058] In an implementation, the rating mark includes attributes, such as, an identifier, ID, of the rated content element, location of the rated content element, end or size or limiter associated with the rated content element, type of applicable rating system, rating value as per the applicable rating system, rating affecting factors in the rated content element. In an implementation, the ID of the rated content element is a logical numeric or meaningful readable string. Furthermore, the location of the rated content element specifies how to find the rated content element within the content item. In an example, the location of the rated content element is a time offset (e.g., in case of video and audio), a deep-link (e.g., in case of video game), and the like. In addition, the end or limiter of the rated content element specifies where the rated content element ends within the content item. For example, the end or limited is a time offset (e.g., in case of video and audio), a terminator tag (e.g., in case of video games), and the like. In addition, the type of applicable rating system is an encoding technique / guideline applied for content rating. In an implementation, the rating value as per the applicable rating system is in the range of 0 to 18+ or more as specified by guidelines of the applicable rating system. The rating affecting factors or the aggregated rating attributes in the rated content element describe the typed factors affecting the rate of the content item, such as violence, bad wording, scare, drugs, smoking, fear, gambling, sex, and the like. Those skilled in the art should appreciate that the number of the rating affecting factors and rating attributes might be expanded, i.e. additional factors might be taken into consideration. In addition, each rating affecting factor has rating value in a range specified by the guidelines of the applicable rating system. Ranges of the rating values may vary per rating affecting factor and / or applicable rating system (e.g. 1 - 5, 0 - 100, 700 - 1000, etc.) provided that the ranges and the rating values are globally unified and understood by all parties. Additional rating attributes or rating affecting factors are added as per specific type of the content item and application specific need, such as list of allowed content modifications (if regulated), and the like. In accordance with an embodiment, each rating mark is based on an extendable markup language and / or have a textual or binary encoding format. In an implementation, the rating mark is in the form of XML, JSON, and the like. An example of the details of the rating mark (or leaf mark) along with corresponding values is shown with following Table 1.

[0059] Table 1 In accordance with an embodiment, the method 100 includes storing a rating manifest for the content item. Moreover, the rating manifest includes one or more rating attributes of the descriptor of each of the rated content elements, the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item. The rating manifest provides a complete picture of an overall rating of the content item. The content item includes a plurality of rated content elements, and each rated content element is assigned the descriptor, rating attributes, and corresponding rating values. The rating manifest is indicative of the overall content rating of the content item, which is beneficial to provide an accurate evaluation of the content item.

[0060] In accordance with an embodiment, the rating value of each of the rated content elements is calculated in accordance with the applicable rating system based on the scores of the rating affecting factors included into the rated content element using weights pre-defined for different types of the rating affecting factors. The weights of the rating affecting factors are numerical values that define the importance of the corresponding rating affecting factor while determining the content rating. The weights for each rating affecting factor are determined before processing the content item. In such embodiment, the weights are defined according to guidelines of the applicable rating system. In addition, the rating value of each rated content element is determined by considering the scores of the rating affecting factors that impact the content rating. The method 100 further includes selecting the applicable rating system and assigning different weights to different types of factors when determining the rating value. By using of using the applicable rating system and predefined weights, the method 100 ensures a consistent and standardized evaluation process, which is beneficial to maintain fairness and objectivity in assessing the content item. Furthermore, the aggregated rating value of the content item is calculated as a function of the rating values of the rated content elements within the content item, or the aggregated rating value is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within the content item using the weights pre-defined for different types of the rating affecting factors. The aggregated rating value is dependent on the rating values of the rated content elements within the content item. In an example, a rated content element includes a first rating affecting factor and a second rating affecting factor. The first rating affecting factor is associated with a first score, for example 60 and the second rating affecting factor is associated with a second score, for example 52. In an implementation, the magnitude of the first score (i.e., 60) is more than the second score (i.e., 52). Further, the first rating affecting factor is having more weightage as compared to the second rating affecting factor. Therefore, the score of the first rating affecting factor is considered as the rating value of the corresponding rated content element.

[0061] In accordance with an embodiment, the method 100 further comprises repeating the steps 102 to 108 for a plurality of content items of a content block to obtain an aggregated descriptor for each of the content items and descriptors for rated content elements within each of the content items. The content block includes the plurality of content items. Moreover, the steps 102 to 108 are repeated for each content item in the content block to obtain descriptors of each rated content element and the aggregated descriptor for each content item from the plurality of content items in the content block. Further, the method 100 includes assigning to the content block an upperlevel aggregated descriptor of a tree structure having the rated content elements as first level nodes, the content items as second level nodes and the content block as an upper-level node. Moreover, each first level node comprises a rating value and / or one or more other rating attributes of a descriptor of corresponding rated content element, each second level node comprises an aggregated rating value and / or one or more other aggregated rating attributes of an aggregated descriptor of corresponding content item. In an example, the tree structure for the rated content elements includes the content block as the upper-level node B, which is includes two content items Cl and C2 as second level nodes. Further, the content item Cl includes two rated content elements SI and S2 as first level nodes, whereas the content item C2 includes two rated content elements S3 and S4 as first level nodes. Here, the content block, B, includes two content items Cl and C2. Further, Cl includes two rated content elements SI and S2. Furthermore, C2 includes two rated content elements S3 and S4. Moreover, the B is upper level node, Cl and C2 are second level nodes and SI, S2, S3 and S4 are first level nodes. The abovementioned tree or list organized structure supports multilevel elements nesting. Every element of the tree structure has corresponding rating mark as well as a list of aggregated or next level rating attributes.

[0062] The rating value and / or the one or more attributes of the descriptor of the rated content elements SI, S2, S3, and S4 in the first level node are determined by following steps from step 102 to step 108 of the method 100. Moreover, the rating values of the rated content elements SI and S2 are combined to determine the aggregated rating value of the content item C 1 and the rating values of the rated content elements S3 and S4 are combined to determine the aggregated rating value of the content item C2. Further, the upper-level node includes applicable rating system and an aggregated rating value of the content block calculated on the aggregated rating values of the content items of the content block or on the scores related to the rating affecting factors included into all the rated content elements within the content items of the content block. In continuation with the previous example, the aggregated rating value of the content block is calculated by two ways - in a first way, the aggregated rating value of the content block is determined by combining the aggregated rating values of content items Cl and C2. In a second way, the aggregated rating value of the content block is determined based on scores of the rating affecting factors included in the scenes SI, S2, S3, and S4. One of the first way and the second way are used to determine aggregated rating value of the content block.

[0063] In accordance with an embodiment, the method 100 includes embedding an upper-level aggregated rating mark into metadata of the content block. In an implementation, the upperlevel aggregated rating mark is indicative of the content rating associated with the content block. Moreover, the metadata of the content block refers to a descriptive information (e.g., date of creation, name, and the like) that provide details about the content item. The upper-level aggregated rating mark includes the aggregated rating value of the content block and / or one or more other elements of the upper-level aggregated descriptor, and / or storing a rating manifest for the content block. In an implementation, the upper-level aggregated rating mark includes the aggregated rating value of the content block, one or more other elements of the upper-level aggregated descriptor, and the rating manifest for the content block. In another implementation, the upper-level aggregated rating mark includes the aggregated rating value of the content block, one or more other elements of the upper-level aggregated descriptor, or the rating manifest for the content block. The aggregated rating value of the content block is indicative of combined rating for all the content items included in the content block. The one or more other elements of the upper-level aggregated descriptor may include list locations of rated content elements within each content item in the content block, the list of applicable rating systems for each content item, rating affecting factors of each content item and the like. Moreover, the rating manifest includes the upper-level aggregated descriptor of the content block. In an implementation, the rating manifest includes the descriptor of the content block along with rating values of the rated content elements and other attributes of content items within the content block. By providing a clear indication of the overall rating through the aggregated rating mark, the metadata of the content block enhances the user experience by providing quick identification of the high-rated content blocks, thereby results in efficient content selection. In accordance with an embodiment, the aggregated rating value of the content block is calculated as a function of the aggregated rating values of the content items of the content block or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within each content item of the content block using weights pre-defined for different types of the rating affecting factors. In one exemplary implementation, the content block includes three content items CI1, CI2, and CI3. The aggregated rating values of the content items CI1, CI2, and CI3 are VI, V2, and V3 respectively. Here, the aggregated rating value of the content block is function of the aggregated rating values VI, V2, and V3. In another exemplary implementation, each content item in the content block includes one or more rating affecting factors that are assigned with corresponding scores. Here, the aggregated rating value of the content block is calculated as a combination of scores of the rating affecting factors and corresponding predefined weights.

[0064] The method 100 includes determining the aggregated rating value of the content item based on scores of ratings affecting factors, which is beneficial to reduce manual errors in content item evaluation as well as reduce the effort and complexity of rating the content item. The combination of aggregated rating values and the ranking manifest in the method 100 is beneficial to provide fine details for content rating evaluation. The method 100 is used for providing an infrastructure to support smooth and efficient information evaluation of the content item as per the local regulation and consumers requirements.

[0065] The steps 102 to 108 are only illustrative, and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.

[0066] FIG. 2 is a flowchart of a method of adapting a content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 2, there is shown a flowchart of a method 200 of adapting a content rating. The method 200 includes steps from 202 to 206.

[0067] The method 200 includes providing adaptation of the content item as per the content rating. In an implementation, the adaptation refers to change, such as removal and / or correction of the content item in accordance with the content rating. For example, if the content rating indicates a definite portion of the content item or whole content item to be inappropriate for a particular audience, then the corresponding portion of the content item or the whole content item is modified to make the content item compliant with viewers' guidelines.

[0068] At step 202, the method 200 includes obtaining rating attributes of a content item that includes rated content elements. Each rated content element enclosing a rating affecting detail. The rated content element refers to the portion or part of the content item, which includes content against socially acceptable norms and the rating affecting detail refers to the content against socially acceptable norms. For example, the content item is a movie, and the rated content elements refer to the scenes in the movie, which depicts violence, bad words, and explicit images. Here, the rating affecting details are “violence”, “bad words” and “explicit images”. Moreover, the rating attributes include, for each rated content element, an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. In continuation with previous example, the position of the rated content element within the content item, (i.e., within the movie) is a timestamp at which the corresponding rated content element, that is, the scene occurs in the content item. The one or more rating affecting factors are expressions, acts or illustrations that are not socially acceptable. The scores related to the rating affecting factors are numerical values that depict the importance of the corresponding rating affecting factor in determining the content rating of the content item.

[0069] At step 204, the method 200 includes calculating an aggregated rating value of the content item based on the rating values of the rated content elements within the content item or on the scores related to the rating affecting factors included into all the rated content elements within the content item. In an implementation, the rating values of the rated content elements are determined as per the guidelines of the applicable rating system, and the aggregated rating value is calculated based on the rating values. In another implementation, the aggregated rating value is determined as a function of the scores related to all the rating affecting factors in the content item. Moreover, the aggregated rating value determines the appropriate audience for the corresponding content item.

[0070] At step 206, the method 200 includes determining, if the aggregated rating value of the content item is above a pre-defined threshold, removing and / or correcting one or more rated content elements within the content item that have top rating values or top scores related to the rating affecting factors included into said rated content elements to decrease the aggregated rating value below the pre-defined threshold. The pre-defined threshold is a numerical limiting value, which acts as a boundary between the socially acceptable or inacceptable content items. For example, the content item is a movie, which includes two scenes, such as a first scene and a second scene. The first scene includes the use of bad words and violence, whereas the second scene includes the use of explicit images and pornography. Here, the method 200 includes calculating the rating value of the first scene on the basis of the applicable rating system or scores of the rating affecting factors “bad words” and “violence”. Further, the method 200 includes calculating the rating value of the second scene on the basis of the applicable rating system or the scores of the rating affecting factors “explicit images” and “pornography”. Further, the aggregated rating value of the content item is determined by combining the rating values of the first scene and the second scene. Further, the aggregated rating value of the content item is compared with the pre-defined threshold. In addition, if the aggregated rating value is more than the pre-defined threshold, then the rated content element having the highest rating value is first removed or corrected and again the aggregated rating value is calculated. Similarly, the rated content elements are removed or corrected in descending order until the aggregated rating value reaches below the pre-defined threshold.

[0071] In accordance with an embodiment, the correcting of each rated content element in the method 200 includes removing a rating affecting factor from said rated content element or adjusting the rating affecting factor to decrease its score. In an example, if the rated content element shows a violent murder scene, such rated content element is removed from the content item or the rated content element is adjusted to make them invisible for the audience. In accordance with an embodiment, the removing or adjusting of the rating affecting factor comprises one or more of an image deletion, an image blurring, an image replacement, a sound muting, and a sound replacement. In an example, the image deletion includes removing the image that depicts a violent scene or blurring images that depict alcohol brands. In another example, the sound from the movie dialogue, which pronounces bad words is muted or replaced with a beep sound. The adjustment or removal of the high rated content item is beneficial to provide a comfortable viewing experience for a wider range of audiences.

[0072] The method 200 includes adjusting or removing rated content items which depict content against socially acceptable norms, thereby preventing the high rated content item to get exposed in front of ineligible audience. Additionally, the method 200 reduces effort and complexity of the content item adaptation and usage enforcement meeting locality specific requirements. Further, the method 200 supports advanced cases, such as automated adaptation, smart content manipulation, and the like to increase accuracy in content rating and manipulation.

[0073] The steps 202 to 206 are only illustrative, and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.

[0074] FIG. 3 is a block diagram that depicts a content processing apparatus, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements from FIG. 1. With reference to FIG. 3 there is shown a block diagram that depicts a content processing apparatus 300 configured for content marking and rating. The content processing apparatus 300 includes a first processor 302, a first memory 304 and a first user interface 306.

[0075] The content processing apparatus 300 is configured for content marking and rating. The content processing apparatus 300 includes the first processor 302. The first processor 302 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions in the method 100. Examples of the first processor 302 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.

[0076] The first memory 304 is configured to store the details about the content rating, which is obtained by processing the content item through the content item processing apparatus. The examples of implementation of the first memory 304 may include, but are not limited to, electrically erasable programmable read-only memory, EEPROM, dynamic random-access memory, DRAM, random access memory, RAM, read-only memory, ROM, hard disk drive, HDD, flash memory, a secure digital, SD, card, solid-state drive, SSD, and / or central processing unit, CPU, cache memory. In an implementation, the content processing apparatus 300 includes a first user interface 306, which is configured to receive content item to be processed. The examples of the first user interface 306 may include, but are not limited to, a compact disc, CD, reader, a computer port, a touch screen, and the like.

[0077] The content processing apparatus 300 is configured to detecting rating affecting details in a content item and defining content parts enclosing the detected details as rated content elements. Further, the content processing apparatus 300 is configured to assign to each of the rated content elements a descriptor with rating attributes. Moreover, the rating attributes include an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. Additionally, the content processing apparatus 300 is configured to assign to the content item an aggregated descriptor with aggregated rating attributes. Moreover, the aggregated rating attributes include a list of the rated content elements with its rating values and / or one or more other rating attributes of its descriptors, applicable rating system and an aggregated rating value of the content item calculated on the rating values of the rated content elements or on the scores related to the rating affecting factors included into all the rated content elements within the content item.

[0078] In an implementation, the rating affecting factors in the descriptor assigned by the content processing apparatus 300 includes one or more of cultural perversions inacceptable by major audiences, pornography, indecent images of children, prohibited images of children, portraits of children in abusive context, cruelty to animals, tobacco advertisements, stirring up hatred, promotion of the criminal activities and drugs, criminal materials, raping, violent behaviour, sadistic attitudes, naturally presented injuries or death.

[0079] In an implementation, the content processing apparatus 300 is configured to embed a rating mark into a metadata of each of the rated content elements. The rating mark includes a rating value and / or one or more other rating attributes of a descriptor of the rated content element. In addition, the content processing apparatus 300 is configured to embed an aggregated rating mark into the metadata of the content item. The aggregated rating mark includes the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item.

[0080] In an implementation, the content processing apparatus 300 is configured to store a rating manifest for the content item. Moreover, the rating manifest includes one or more rating attributes of the descriptor of each of the rated content elements, the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item.

[0081] In an implementation, the content processing apparatus 300 is configured calculate the rating value of each of the rated content elements in accordance with the applicable rating system based on the scores of the rating affecting factors included into the rated content element using weights pre-defined for different types of the rating affecting factors. Furthermore, the content processing apparatus 300 is configured to calculate the aggregated rating value of the content item as a function of the rating values of the rated content elements within the content item or in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within the content item using the weights pre-defined for different types of the rating affecting factors.

[0082] In an implementation, the content processing apparatus 300 is configured to repeating the steps of the method 100 for a plurality of content items of a content block to obtain an aggregated descriptor for each of the content items and descriptors for rated content elements within each of the content items. The content processing apparatus 300 is configured to assign to the content block an upper-level aggregated descriptor of a tree structure having the rated content elements as first level nodes, the content items as second level nodes and the content block as an upperlevel node. Moreover, each first level node includes a rating value and / or one or more other rating attributes of a descriptor of corresponding rated content element. In addition, each second level node includes an aggregated rating value and / or one or more other aggregated rating attributes of an aggregated descriptor of corresponding content item, and the upper-level node comprises applicable rating system and an aggregated rating value of the content block calculated on the aggregated rating values of the content items of the content block or on the scores related to the rating affecting factors included into all the rated content elements within the content items of the content block.

[0083] In an implementation, the content processing apparatus 300 is configured to embed an upperlevel aggregated rating mark into the metadata of the content block. Further, the upper-level aggregated rating mark includes the aggregated rating value of the content block and / or one or more other elements of the upper-level aggregated descriptor, and / or storing a rating manifest for the content block. Moreover, the rating manifest includes the upper-level aggregated descriptor of the content block. In an implementation, the content processing apparatus 300 is configured to calculate the aggregated rating value of the content block as a function of the aggregated rating values of the content items of the content block or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within each content item of the content block using weights pre-defined for different types of the rating affecting factors.

[0084] In an implementation, each rating mark embed by the content processing apparatus 300 is based on an extendable markup language and / or have a textual or binary encoding format.

[0085] The content processing apparatus 300 is configured to determine the aggregated rating value of the content item based on scores of ratings affecting factors, which is beneficial to reduce manual errors in content item evaluation as well as reduce effort and complexity of rating the content item. The combination of aggregated rating values and the ranking manifest performed by the content processing apparatus 300 is beneficial to provide fine details for content rating evaluation. The content processing apparatus 300 provides an infrastructure to support smooth and efficient information evaluation of the content item as per the local regulation and consumers requirements.

[0086] FIG. 4 is a block diagram that depicts a content processing apparatus, in accordance with a different embodiment of the present disclosure. FIG. 4 is described in conjunction with elements from FIG. 2. With reference to FIG. 4, there is shown a block diagram that depicts a content processing apparatus 400 configured for adapting a content rating. The content processing apparatus 400 includes a second processor 402, a second memory 404 and a second user interface 406.

[0087] The content processing apparatus 400 is configured for adapting a content rating of a content item. The content processing apparatus 400 includes the second processor 402. The second processor 402 may refer to one or more individual processors, processing devices, and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices, and elements are arranged in various architectures for responding to and processing the instructions in the method 200. Examples of the second processor 402 may include but are not limited to, a hardware processor, a digital signal processor (DSP), a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an application-specific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, a data processing unit, a graphics processing unit (GPU), and other processors or control circuitry.

[0088] The second memory 404 is configured to store the content item obtained after adapting. The examples of implementation of the second memory 404 may include, but are not limited to, electrically erasable programmable read-only memory, EEPROM, dynamic random-access memory, DRAM, random access memory, RAM, read-only memory, ROM, hard disk drive, HDD, flash memory, a secure digital, SD, card, solid-state drive, SSD, and / or central processing unit, CPU cache memory. In an implementation, the content processing apparatus 400 includes a second user interface 406, which is configured to receive the content item for ad. The examples of the second user interface 406 may include, but are not limited to, a compact disc, CD reader, a touch screen interface, a computer port, and the like.

[0089] The content processing apparatus 400 is configured to obtain rating attributes of a content item that includes rated content elements. Further, each rated content element enclosing a rating affecting detail. Moreover, the rating attributes comprise, for each rated content element, an information about a position of the rated content element within the content item, one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores. Furthermore, the content processing apparatus 400 is configured to calculate an aggregated rating value of the content item based on the rating values of the rated content elements within the content item or on the scores related to the rating affecting factors included into all the rated content elements within the content item. In addition, if the aggregated rating value of the content item is above a pre-defined threshold, the content processing apparatus 400 is configured to remove and / or correct one or more rated content elements within the content item that have top rating values or top scores related to the rating affecting factors included into said rated content elements to decrease the aggregated rating value below the pre-defined threshold.

[0090] In an implementation, the correcting of each rated content element by the content processing apparatus 400 includes removing a rating affecting factor from said rated content element or adjusting the rating affecting factor to decrease its score. In an implementation, the removing or adjusting of the rating affecting factor by the content processing apparatus 400 includes one or more of an image deletion, an image blurring, an image replacement, a sound muting, and a sound replacement.

[0091] The content processing apparatus 400 includes adjusting or removing rated content items which depict content against socially acceptable norms, thereby preventing the high rated content item to get exposed in front of an ineligible audience. Additionally, the content processing apparatus 400 reduces the effort and complexity of the content item adaptation and usage enforcement meeting locality- specific requirements. Further, the content processing apparatus 400 supports advanced cases, such as automated adaptation, smart content manipulation, and the like to increase accuracy in content rating and manipulation.

[0092] FIG. 5 is a block diagram that depicts an exemplary scenario of a rated content element along with a descriptor in the method for content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 5, there is shown a block diagram 500 that depicts a rated content element along with a descriptor 504 in the method 100 (of FIG.1) of content rating.

[0093] With reference to FIG. 5, there is shown the exemplary scenario of the rated content element of a content item as a scene 2.2 502. Here, the content item is a movie, and the rated content element is the scene 2.2 502. Moreover, during execution of the method 100, the scene 2.2 502 is assigned the descriptor 504 that includes identifier, ID of the rated content element (i.e., the scene 2.2 502) as 2.2.2, and the rated content element shows a bank robbery scene. Further, the descriptor 504 includes the location of the scene 2.2 502 in the content item as 45’ 12.123 and the size of the scene 2.2 502 as 3’41.542. In addition, the descriptor 504 includes type of applicable rating system, such as PEGI and the rating value of the scene 2.2 502 as per the applicable rating system as 12. Further, the descriptor 504 includes one or more attributes with rating affecting factors found in the rated content element as “violence”, “drugs”, “bad language” and corresponding scores as “64”, “32” and “48” respectively.

[0094] FIG. 6 is a block diagram that depicts an exemplary scenario of content splitting in the method of content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 6, there is shown a block diagram 600 that depicts an exemplary scenario of content splitting in the method 100 (of FIG. 1) of content rating. In the exemplary scenario, the content item 602 is a movie and the rated content elements of the movie are scene 1 604, scene 2 606, and up to scene N 608. The scene 2 604 further includes two subparts such as scene 2.1 610 and scene 2.2 612.

[0095] FIGs. 7A, 7B, and 7C are different diagrams that depict decomposition of a content item and marking with rating values in the method of content rating, in accordance different embodiments of the present disclosure. With reference to FIGs. 7A, 7B and 7C, there are shown diagrams 700A, 700B, and 700C that depict decomposition of a content item 702 and marking with rating values in the method 100 (of FIG. 1) of content rating. With reference to FIG. 7A, the content item 702 is decomposed, and a definite number of scenes are mapped as rated content elements based on the content being presented through the corresponding scenes. There is further shown that one or more essential rate elements events are mapped. With reference to FIG. 7B, the content item 702 is labelled as El, and rated content elements are labelled as rated content element El.1 704, rated content element El.2 706, and rated content element El.3 708. With reference to FIG. 7C, the rating affecting factors for each rated content element are identified and corresponding scores are calculated in the method 100. For example, the rating affecting factors in the rated content element El.l 704 are “Drugs” and “violence”, with scores as “37” and “18” respectively. Further, for the rated content element El.2 706, the rating affecting factors are “drugs” and “bad wording” with corresponding scores “36” and “32” respectively. Similarly for the rated content element E 1.3 708, the rating affecting factors are “violence” and “bad wording” with corresponding scores “6” and “53” respectively. Here, the combined rating affecting factors of the content item 702 (i.e., El) are based on the rating affecting factors of the rated content element El.l 704, the rated content element El.2 706, and the rated content element El.3 708. Therefore, the rating affecting factor “drugs” is having the highest score of “37” (in the rated content element El. l 704) amongst all the rated content elements. Similarly, the rating affecting factors “bad wordings” and “violence” are having highest scores of “53” and “18” respectively, as shown in FIG. 7C. In such case, the rating value of the content item is selected as “53” as it is highest amongst all other scores of the rating affecting factor. Further, based on the rating value of “53”, the content item is assigned rating of “18”, that is the content item is suitable for audience above 18 years of age.

[0096] FIG. 8 is a flowchart that depicts a method for content decomposition and marking with rate, in accordance with an embodiment of the present disclosure. With reference to FIG. 8, there is shown a flowchart that depicts a method 800 for content decomposition and marking with rate. The method 800 includes steps from 802 to 816. The method 800 starts at step 802. At step 804, the method 800 includes matching an essential event within the content item. The essential event refers to the parts of the content item that shows expressions, which are socially unacceptable, such as violence, drugs, bad wordings, and the like.

[0097] At step 806, the method 800 includes defining rated content elements based on the essential events within the content item.

[0098] At step 808, the method 800 includes specifying typed rates for the rated content elements. The typed rates are values assigned to the rated content element according to applicable rating system.

[0099] At step 810 and 812, the method 800 includes calculating integral scores of the rating affecting factors in all the rated content elements within the content item. The scores are numerical values assigned to the one or more rating affecting factors, such as “violence”, “bad wordings”, and the like.

[0100] At step 814, the method 800 includes calculating an overall rate of the content item. The overall rate refers to the aggregated rating value (as mentioned in the method 100) of the content item based on a combination of integral scores of all the rating affecting factors in the content item.

[0101] At step 816, the method 800 includes embedding an aggregated rating mark into the content item and creating a rating manifest for the content item. The aggregated rating mark includes the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item. The rating manifest includes one or more rating attributes of the descriptor of each of the rated content elements, the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item. Thereafter, the method 800 ends at step 818.

[0102] FIG. 9 is a diagram that depicts an exemplary scenario of defining an aggregated rating value of the content item in the method of content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 9, there is shown a diagram 900 depicts an exemplary scenario of defining an aggregated rating value of the content item in the method 100 (of FIG. 1) of content rating.

[0103] In the exemplary scenario, the content item is a movie 902 and the rated content elements of the movie are a scene 1 904, a scene 2 906, and up to a scene N 908. The scene 2 904 further includes two subparts, such as scene 2.1 910 and scene 2.2 912. Further, each of the rated content elements is assigned with two types of numerical values, first type is a rating value according to applicable rating system and second type is based on scores of the rating affecting factors included in the rated content elements. For the scene 2.1 910, the numerical values are “8” and “81”, for the scene 2.2 912, the numerical values are “15”, “11”. The maximum numerical value among the scene 2.1 910 and the scene 2.2 912 is “81”. Therefore, a rating value of “81” is assigned to the scene 2 906. Further, the scene 1 904 is assigned with numerical values “22”, “45”, and the scene N 908 is assigned with numerical values “0” and “53”. Here, the highest numerical value for first type of numerical value is “22” and the highest numerical value for second type of numerical value is “81”. Further, based on the numerical values “22” and “81”, the aggregated rating value of “12” is assigned to the content item based on the applicable rating system PEGI.

[0104] FIG. 10 is a diagram that depicts an exemplary scenario of assigning pre-defined weights to one or more rating affecting factors in the method of content rating, in accordance with an embodiment of the present disclosure. With reference to FIG. 10, there is shown a diagram 1000 that depicts assigning pre-defined weights to one or more rating affecting factors in the method of content rating.

[0105] In the exemplary scenario, a rated content element 1002 includes a plurality of rating affecting factors such as 1004, 1006, 1008, 1010, 1012, 1014, 1016, and 1018. The rating affecting factors 1004, 1006, 1008, 1010, 1012, 1014, 1016, and 1018 are assigned with pre-defined weights as 1.4, 4.2, 5.1, 3.0, up to 4.1 respectively. The weights are adjusted by taking into consideration what is more or less acceptable in a specific locality. For example, the rating affecting factor “bad wordings” is more tolerated than the rating affecting factor “drugs”. Therefore, the rating affecting factor “bad wordings” is assigned a weight of more magnitude than the rating affecting factor “drugs”.

[0106] FIG. 11 is a flowchart that depicts a method for rate adaptation, in accordance with an embodiment of the present disclosure. With reference to FIG. 11, there is shown a flowchart that depicts a method 1100 for rate adaptation. The method 1100 includes steps from 1002. The method 1100 starts at step 1102.

[0107] At step 1104, the method 1100 includes calculating the aggregated rating value of all the rated content elements within the content item. At step 1106, the method 1100 includes comparing the aggregated rating value with allowable or pre-defined threshold.

[0108] If the aggregated rating value is above the allowable or pre-defined threshold, a step 1108 in the method 1100 gets executed. At the step 1108, the method 1100 includes switching policy of the content item. The switching policy of the content item refers to changed or adaptation of the content item. The switching policy of the content item is performed in one of steps 1110 or 1112. At the step 1110, the method 1100 includes removing the rated content element having high rating values. At the step 1112, the method 1100 includes application of adapt factor to the rated content element. The adapt factor refers to operations such as image blurring, voice beeping and the like. After execution of the steps 1110 and / or 1112, the steps 1104 and 1106 are again executed to calculate a modified aggregated rating value of a modified content item and compare the aggregated rating value with the allowable or pre-defined threshold. The switching policy of the content item is performed until the aggregated rating value reaches below the allowable or pre-defined threshold. The steps 1104, 1106, 1108, 1110, and / or 1112 are performed until the aggregated rating value decreases below the allowable or pre-defined threshold.

[0109] The method 1100 ends at step 1114. Moreover, if the aggregated rating value determined in the step 1104 is lesser than the allowable or predefined threshold, then the step 1114 is directly followed after step 1104, without undergoing through steps 1106, 1108, 1110, and 1112.

[0110] FIG. 12 is a diagram that depicts an exemplary scenario of rate adaptation in the method of adapting a content rating, in accordance with an embodiment of the present disclosure. With reference to FIG.12, there is shown a diagram 1200 that depicts an exemplary scenario of rate adaptation in the method 200 (of FIG. 2) of adapting a content rating.

[0111] In the exemplary scenario, the content item is the movie 902 and the rated content elements of the movie 902 are scene 1 904, scene 2 906, and up to scene N 908. The scene 2 906 further includes two subparts such as scene 2.1 910 and scene 2.2912. Further, each of the rated content elements is assigned with two types of numerical values, first type is a rating value according to applicable rating system and second type is based on scores of the rating affecting factors included in the rated content elements. For scene 2.1 910, the numerical values are 8 and 81, for scene 2.2 912, the numerical values are 15, 11. The maximum numerical value among the scene 2.1 910 and the scene 2.2 912 is 81. Therefore, a rating value of 81 is assigned to the scene 2 906. Further, the scene 1 904 is assigned with numerical values 22, 45, and the scene N 908 is assigned with numerical values 0 and 53. Here, the highest numerical value for first type of numerical value is “22” and the highest numerical value for second type of numerical value is “81”. Further, based on the numerical values “22” and “81”, the aggregated rating value of “12” is assigned to the content item based on the applicable rating system PEGI. Here, the rating value of the rated content element scene 2.1 is maximum of all rating values, that is 81. Therefore, during the rate adaptation, the method 200 includes removing the rated content element scene 2.1. Further, due to removal of the scene 2.1 910, the scene 2 906 attains rating value of 11 and the movie 902 attains the rating value 53, which is lesser than 81. Further, the method 200 includes obtaining the aggregated rating value of the movie 902 as PEGI 8. Here, before removal of the scene 2.1 910, the aggregated rating value was PEGI 12 and after removal of the scene 2.1 910, the aggregated rating value becomes PEGI 8. The process of removing the rated content elements from the content item is continued till the aggregated rating value becomes lesser than the pre-defined threshold.

[0112] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe, and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments. The word "optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.

Claims

CLAIMS1. A method (100) of content rating, the method (100) comprising: detecting rating affecting details in a content item (602,702), defining content parts enclosing the detected details as rated content elements, assigning to each of the rated content elements a descriptor with rating attributes, wherein the rating attributes comprise an information about a position of the rated content element within the content item (602, 702), one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores, and assigning to the content item (602, 702) an aggregated descriptor with aggregated rating attributes, wherein the aggregated rating attributes comprise a list of the rated content elements with its rating values and / or one or more other rating attributes of its descriptors, applicable rating system and an aggregated rating value of the content item (602, 702) calculated on the rating values of the rated content elements or on the scores related to the rating affecting factors included into all the rated content elements within the content item (602, 702).

2. The method (100) of claim 1, further comprising: embedding a rating mark into a metadata of each of the rated content elements, the rating mark comprising a rating value and / or one or more other rating attributes of a descriptor of the rated content element, and embedding an aggregated rating mark into metadata of the content item (602, 702), the aggregated rating mark comprising the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item (602, 702).

3. The method (100) of claim 1 or 2, further comprising: storing a rating manifest for the content item (602, 702), wherein the rating manifest comprises one or more rating attributes of the descriptor of each of the rated content elements, the aggregated rating value and / or one or more other aggregated rating attributes of the aggregated descriptor of the content item (602, 702).

4. The method (100) of any of claims 1 to 3, wherein the rating value of each of the rated content elements is calculated in accordance with the applicable rating system based on the scores ofthe rating affecting factors included into the rated content element using weights pre-defined for different types of the rating affecting factors, and the aggregated rating value of the content item (602, 702) is calculated as a function of the rating values of the rated content elements within the content item (602, 702) or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within the content item (602, 702) using the weights pre-defined for different types of the rating affecting factors.

5. The method (100) of any of claims 1 to 4, further comprising: repeating the steps of claim 1 for a plurality of content items of a content block to obtain an aggregated descriptor for each of the content items and descriptors for rated content elements within each of the content items, assigning to the content block an upper-level aggregated descriptor of a tree structure having the rated content elements as first level nodes, the content items as second level nodes and the content block as an upper-level node, wherein each first level node comprises a rating value and / or one or more other rating attributes of a descriptor of corresponding rated content element, each second level node comprises an aggregated rating value and / or one or more other aggregated rating attributes of an aggregated descriptor of corresponding content item, and the upper-level node comprises applicable rating system and an aggregated rating value of the content block calculated on the aggregated rating values of the content items of the content block or on the scores related to the rating affecting factors included into all the rated content elements within the content items of the content block.

6. The method (100) of claim 5, further comprising: embedding an upper-level aggregated rating mark into metadata of the content block, the upper-level aggregated rating mark comprising the aggregated rating value of the content block and / or one or more other elements of the upper-level aggregated descriptor, and / or storing a rating manifest for the content block, wherein the rating manifest comprises the upper-level aggregated descriptor of the content block.

7. The method (100) of claim 5 or 6, wherein the aggregated rating value of the content block is calculated as a function of the aggregated rating values of the content items of the content block or it is calculated in accordance with the applicable rating system based on maximum scores related to each type of the rating affecting factors among the rated content elements within each content item of the content block using weights pre-defined for different types of the rating affecting factors.

8. The method (100) of any of claims 2 to 7, wherein each rating mark is based on an extendable markup language and / or have a textual or binary encoding format.

9. The method (100) of any of claims 1 to 8, wherein the rating affecting factors comprise one or more of cultural perversions inacceptable by major audiences; pornography; indecent images of children; prohibited images of children; portraits of children in abusive context; cruelty to animals; tobacco advertisements; stirring up hatred; promotion of the criminal activities and drugs; criminal materials; raping, violent behavior, sadistic attitudes; naturally presented injuries or death.

10. A method (200) of adapting a content rating, the method (200) comprising: obtaining rating attributes of a content item (602, 702) that comprises rated content elements, each rated content element enclosing a rating affecting detail, wherein the rating attributes comprise, for each rated content element, an information about a position of the rated content element within the content item (602, 702), one or more rating affecting factors included into the rated content element, scores related to the rating affecting factors, applicable rating system and a rating value calculated on the scores, calculating an aggregated rating value of the content item (602, 702) based on the rating values of the rated content elements within the content item (602, 702) or on the scores related to the rating affecting factors included into all the rated content elements within the content item, and if the aggregated rating value of the content item (602, 702) is above a pre-defined threshold, removing and / or correcting one or more rated content elements within the content item (602, 702) that have top rating values or top scores related to the ratingaffecting factors included into said rated content elements to decrease the aggregated rating value below the pre-defined threshold.

11. The method (200) of claim 10, wherein the correcting of each rated content element comprises removing a rating affecting factor from said rated content element or adjusting the rating affecting factor to decrease its score.

12. The method (200) of claim 11, wherein the removing or adjusting of the rating affecting factor comprises one or more of an image deletion, an image blurring, an image replacement, a sound muting and a sound replacement.

13. A content processing apparatus (300) configured for content marking and rating in accordance with any of claims 1 to 9.

14. A content processing apparatus (400) configured for adapting a content rating in accordance with any of claims 10 to 12.

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