Method and apparatuses for personalizing textual content in endpoint device and on content provider side

Abstract

To personalize textual content for a user a user-specific information is obtained in an endpoint device and a default comfort readability score, CRDS, of the user is determined by a CRDS calculator. The default CRDS is then embedded to a first content request sent to a content provider and the content provider calculates a readability score, RDS, of the requested textual content. The requested textual content is adapted to correspond to the default CRDS, if the RDS does not match the default CRDS, and the textual content adapted to the default CRDS is provided to the endpoint device together with characteristics of the requested textual content available for the content provider. This allows adapting the textual content for the user with maintaining the privacy of the user.

Description

[0001] METHOD AND APPARATUSES FOR PERSONALIZING TEXTUAL CONTENT IN ENDPOINT DEVICE AND ON CONTENT PROVIDER SIDE

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to the field of computational linguistics and, more specifically, to a method of personalizing textual content for a user. Furthermore, the present disclosure relates to apparatuses for personalizing textual content in an endpoint device and on a content provider side.

[0004] BACKGROUND

[0005] Typically, in today's digital age, the quality and ease of comprehension of the textual content may significantly impact user engagement and satisfaction. The ease of text understanding is important for both users (which read the content) and the content providers. The textual content which is confusing and difficult to comprehend can compel users to seek alternative sites, products, services, or similar options. It could also lead to detrimental outcomes caused by erroneous decisions or actions. Typically, to improve readability of the textual content, the content providers create the textual content with the average targeted audience in mind, which does not address below average skilled users. Further, the users utilize a variety of third-party simplification tools and services, which involves use of additional operators and services that eventually leads to high cost. Thus, the conventional approaches used by the users and the content providers do not provide comprehensive assessment of the textual content for users and also can lead to errors in the adaptation of the textual content by the content providers.

[0006] Currently, certain attempts have been made to somehow increase the comprehensive assessment and text adaptation by using technologies such as text simplification, which involves operations, for example, splitting long sentences, replacing words with synonyms, evaluating the user’s skills, language preferences psychological conditions and the like. Currently, the industry's focus is on the manual adaptation of texts for targeted audiences by content providers, as well as user-driven customization through utility-based services and cloud platforms. Additionally, conventional approaches include employing local tools for text adaptation, which results in significant burdens on computer systems, licensing expenses, and maintenance costs for these tools. Moreover, content providers resort to third-party services for text adaptation, leading to challenges like high service costs, accessibility issues, and privacy concerns. However, such attempts are suboptimal, i.e., the text adaptation does not match specific user comprehension level and involves privacy issues from the user side. Thus, there exists a technical problem of how to minimize errors in the text adaptation and personalize the textual content based on the user's skills while maintaining privacy of the user.

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

[0008] SUMMARY

[0009] The present disclosure provides a method of personalizing textual content for a user and apparatuses for personalizing textual content in an endpoint device and on a content provider side. The present disclosure provides a solution to the existing problem of how to minimize errors in the text adaptation and personalize the textual content based on the user's skills while maintaining privacy of the user. 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 personalizing textual content for a user and improved apparatuses for personalizing textual content in the endpoint device and on the content provider side, for example, by determining user specific readability score and adapting the textual content based on the corresponding score to personalize the textual content.

[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 one aspect, the present disclosure provides a method of personalizing textual content for a user. The method comprises obtaining a user-specific information about preferences and reading-related behavior of a user by a user metering module of an endpoint device and determining a default comfort readability score, CRDS, of the user by a CRDS calculator of the endpoint device based on the user-specific information. Furthermore, the method comprises sending, by the endpoint device to a content provider, a first content request to request a textual content, the first content request comprising the default CRDS of the user embedded thereto by a CRDS encoder of the endpoint device and calculating a readability score, RDS, of the requested textual content by the content provider. In addition, the method includes adapting the requested textual content by the content provider to correspond to the default CRDS, if the RDS of the requested textual content does not match the default CRDS of the user, and providing the endpoint device with the textual content adapted to the default CRDS by the content provider together with characteristics of the requested textual content available for the content provider.

[0012] The method involves determining the default CRDS of the user and adapting the textual content provided by the content provider in accordance with the default CRDS, which minimizes errors in the adaptation of the textual content. The method involves dynamic CRDS adjustment (based on the evaluation of the matching degree between the characteristics of the textual content and the user), which ensures that the user receives the textual content that aligns with the user’s reading comfort level and interests. The structure of negotiation mechanisms (or protocol) used in the method, that is, the exchange of CRDS between the endpoint device and the content provider maintains privacy of the user. The method helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0013] In an implementation form, the determining of the default CRDS of the user comprises one or more of the CRDS calculator calculating the default CRDS based on a user grade specified by the user, the CRDS calculator calculating the default CRDS based on the user-specific information, and the CRDS calculator calculating the default CRDS dynamically using an information about a service provider the user is going to request for a textual content and the user-specific information.

[0014] By allowing the CRDS calculator to consider the user's specified grade level, the method provides flexibility for users to have control over their reading preferences. The dynamic calculation of the default CRDS based on the user's intended service provider and user-specific information ensures adaptability to different content sources.

[0015] In another implementation form, the method further comprises calculating an adjusted CRDS of the user for the requested textual content by the CRDS calculator of the endpoint device based on a comparison of the characteristics of the requested textual content received from the content provider and related characteristics of the user as per the user-specific information. Further, the method comprises sending, by the endpoint device to the content provider, a second content request to request the textual content, the second content request comprising the adjusted CRDS of the user embedded thereto by the CRDS encoder. In addition, the method comprises adapting the requested textual content by the content provider to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user, and providing the endpoint device with the textual content adapted to the adjusted CRDS by the content provider.

[0016] By calculating an adjusted CRDS based on the comparison of content characteristics and user preferences, the method ensures that the requested textual content is personalized and adapted to the user's individual reading comfort level. Such adaptation considers the user's specific interests, language preferences, and reading habits, providing content that is better suited to the user's needs and preferences.

[0017] In yet another implementation form, the calculating of the adjusted CRDS of the user comprises the CRDS calculator multiplying the default CRDS of the user by a comprehension score, CS. Moreover, the CS is a function of a similarity degree between the characteristics of the requested textual content and the related characteristics of the user, the CS function being defined so that the CS is above one if the user is more capable to comprehend the requested textual content and below one if the user is less capable to comprehend the requested textual content.

[0018] The use of the comprehension score enables the CRDS calculator to select content more effectively. By considering how well the content aligns with the user's preferences, the method can ensure that the user is presented with content that matches the user’s reading capabilities and interests.

[0019] In an implementation form, the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content and the related characteristics of the user.

[0020] By considering distance measurements, percentage of matched topics, and weighed word frequencies, the CS function offers a more comprehensive and accurate content matching process, which leads to a better selection of content that closely aligns with the user's preferences and interests.

[0021] In another implementation form, the user-specific information comprises one or more of a language that the user speaks, geographic area where the user is based, browsing history of the user, an education level, an expertise domain and a part of thesaurus of the user; and the characteristics of the requested textual content provided by the content provider comprise a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content.

[0022] By considering user-specific information such as the language spoken, geographic area, browsing history, education level, expertise domain, and thesaurus usage, the method can offer personalized content recommendations, which ensures that the content provided to the user is relevant, culturally appropriate, and aligns with the user's interests and expertise.

[0023] In yet another implementation form, the method comprises updating the default CRDS of the user by the CRDS calculator of the endpoint device based on an update of the user-specific information.

[0024] By updating the default CRDS using the latest user-specific information, the method ensures that the user's current reading preferences and habits are accurately reflected. As the user's interests and reading behaviors may change over time, updating the default CRDS allows the system to stay up to date with the user's evolving preferences.

[0025] In an implementation form, the first content request comprises a GET request including a Uniform Resource Locator, URL, of the requested textual content and the default CRDS of the user appended to the URL. Moreover, the default CRDS comprises one or more typed values of the user comfort readability score according to one of readability metrics including Flesch- Kincaid Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score.

[0026] The use of the default CRDS appended to the URL in the GET request allows for an enhanced personalized content retrieval. Further, by incorporating one or more typed values of the user's comfort readability score, based on various readability metrics, the system can intelligently identify the user's preferred level of textual complexity and adapt the content accordingly. In another implementation form, each typed value of the user comfort readability score comprises a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value.

[0027] The inclusion of a readability metric identifier, readability score value, and weight for each typed value of the user comfort readability score allows for a precise and customizable assessing and prioritizing textual content based on various readability metrics.

[0028] In yet another implementation form, the second content request comprises a GET request including the URL of the requested textual content and the adjusted CRDS of the user appended to the URL. Moreover, the adjusted CRDS has the same format as the default CRDS.

[0029] In this implementation, by including the adjusted CRDS of the user, formatted similarly to the default CRDS, in the second content request's GET request URL, the system ensures continuous personalization of textual content, enabling a seamless and consistent user experience based on their readability preferences.

[0030] In an implementation form, the characteristics of the textual content are embedded into a Hypertext Markup Language, HTML, page element of a web page with the textual content.

[0031] By embedding the characteristics of the textual content into HTML page element of the web page with the textual content, a seamless integration and presentation of personalized content on web pages is enabled enhancing user experience and readability.

[0032] In another aspect, the present disclosure provides an apparatus for personalizing textual content in an endpoint device. The apparatus includes a user metering module configured for obtaining a user-specific information about preferences and reading-related behavior of a user of the endpoint device and a comfort readability score, CRDS, calculator configured for determining a default CRDS of the user based on the user-specific information. In addition, the apparatus includes a CRDS encoder configured for embedding the default CRDS of the user into a first content request that is sent by the endpoint device to a content provider to request a textual content. In addition, the endpoint device is configured for receiving the textual content adapted to the default CRDS from the content provider together with characteristics of the requested textual content available for the content provider. The apparatus determines the default CRDS of the user and adapts the textual content provided by the content provider in accordance with the default CRDS, which minimizes errors in the adaptation of the textual content. The apparatus is configured to perform dynamic CRDS adjustment, which ensures that the user receives the textual content that aligns with corresponding reading comfort level and interests of the user, thereby making the reading experience more enjoyable and engaging. The structure of negotiation mechanisms, such as the exchange of CRDS used in the apparatus between the endpoint device and the content provider, maintains privacy of the user. The apparatus helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0033] In an implementation form, the CRDS calculator is configured for determining the default CRDS of the user by one or more of calculating the default CRDS based on a user grade specified by the user, calculating the default CRDS based on the user-specific information, and calculating the default CRDS dynamically using an information about a service provider the user is going to request for a textual content and the user-specific information.

[0034] The apparatus ensures adaptability to different content sources or content providers by use of the dynamic calculation of the default CRDS.

[0035] In another implementation form, the CRDS calculator is configured for calculating an adjusted CRDS of the user for the requested textual content based on a comparison of the characteristics of the requested textual content received from the content provider and related characteristics of the user as per the user-specific information. In addition, the CRDS calculator is configured for updating the default CRDS of the user based on an update of the user-specific information and the endpoint device is configured for sending a second content request to the content provider to request the textual content if the adjusted CRDS differs from the default CRDS by a predefined threshold. Furthermore, the CRDS encoder is configured for embedding the adjusted CRDS of the user into the second content request that is sent by the endpoint device to the content provider, and the endpoint device is configured for receiving the textual content adapted to the adjusted CRDS from the content provider.

[0036] In an implementation form, the CRDS calculator is configured for calculating the adjusted CRDS of the user by multiplying the default CRDS of the user by a comprehension score, CS. Moreover, the CS is a function of a similarity degree between the characteristics of the requested textual content and the related characteristics of the user, the CS function is defined so that the CS is above one if the user is more capable to comprehend the requested textual content and below one if the user is less capable to comprehend the requested textual content, and the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content and the related characteristics of the user.

[0037] In another implementation form, the first content request includes a GET request including a Uniform Resource Locator, URL, of the requested textual content. Further, the CRDS encoder is configured for appending the default CRDS of the user to the URL in the GET request. Moreover, the default CRDS includes one or more typed values of the user comfort readability score according to one of readability metrics including Flesch-Kincaid reading Ease, Flesch- Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure Of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score, and each typed value of the user comfort readability score comprises a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value.

[0038] In yet another implementation, the second content request includes a GET request including the URL of the requested textual content, and the CRDS encoder is configured for appending the adjusted CRDS of the user to the URL in the GET request. Moreover, the adjusted CRDS has the same format as the default CRDS.

[0039] By appending the adjusted CRDS of the user to the URL in the GET request, the data transfer process in the apparatus becomes more efficient. Such adjusted CRDS are conveyed directly within the URL, eliminating the need for separate data packaging or additional request headers. Such a streamlined approach of the apparatus reduces data overhead and improves the overall efficiency of the content retrieval process.

[0040] In yet another aspect, the present disclosure provides an apparatus for personalizing textual content on a content provider side. The apparatus comprises a readability score, RDS, evaluator configured for calculating an RDS of a textual content in reply to receiving by a content provider a first content request from an endpoint device, the first content request requesting the textual content and embedding a default CRDS of a user of the endpoint device. Further, the apparatus comprises an adaptive transformer configured for adapting the requested textual content to correspond to the default CRDS, if the RDS of the requested textual content does not match the default CRDS of the user. In addition, the content provider is configured for providing the endpoint device with the textual content adapted to the default CRDS together with characteristics of the requested textual content that comprise a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content.

[0041] The RDS evaluator in the apparatus determines the RDS of the textual content and adapts the textual content to match with the default CRDS of the user. This minimizes errors in the adaptation of the textual content and enables personalization of the textual content based on the user's skills. The adaptive transformer in the apparatus is configured to perform dynamic CRDS adjustment, which ensures that the user receives the textual content that aligns with corresponding reading comfort level and interests, thereby making the reading experience more enjoyable and engaging. The exchange of CRDS used in the apparatus between the endpoint device and the content provider ensures that privacy of the user is maintained. The apparatus helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0042] In an implementation, the content provider is configured for receiving a second content request for the textual content from the endpoint device, the second content request embedding an adjusted CRDS of the user of the endpoint device. Further, the adaptive transformer is configured for adapting the requested textual content to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user. The content provider is further configured for providing the endpoint device with the textual content adapted to the adjusted CRDS of the user.

[0043] In another implementation, the RDS evaluator is further configured for embedding characteristics of the textual content into a Hypertext Markup Language, HTML, page element of a web page with the textual content.

[0044] 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.

[0045] 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.

[0046] BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[0049] FIG. 1 is a flow chart of a method of personalizing textual content for a user, in accordance with an embodiment of the present disclosure;

[0050] FIG. 2 is a block diagram that depicts an apparatus for personalizing textual content in an endpoint device, in accordance with an embodiment of the present disclosure;

[0051] FIG. 3 is a block diagram that depicts an apparatus for personalizing textual content on a content provider side, in accordance with an embodiment of the present disclosure; FIG. 4 is a diagram illustrating an exemplary scenario of operations of an apparatus for personalizing textual content in an endpoint device and an apparatus for personalizing textual content on a content provider side, in accordance with an embodiment of the present disclosure;

[0052] FIG. 5 is a diagram illustrating an exemplary scenario of negotiation capabilities and CRDS adjustments between an endpoint device and an adaptive Tenderer, in accordance with an embodiment of the present disclosure;

[0053] FIG. 6 is a diagram illustrating an adjustment flow of a default CRDS between a browser on an endpoint device and a server associated with a content provider, in accordance with an embodiment of the present disclosure; and

[0054] FIG. 7 is a diagram illustrating that depicts an apparatus for personalizing textual content in an endpoint device, in accordance with a different embodiment of the present disclosure;

[0055] 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.

[0056] DETAILED DESCRIPTION OF EMBODIMENTS

[0057] 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 practicing the present disclosure are also possible.

[0058] FIG. 1 is a flow chart of a method of personalizing textual content for a user, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a flow chart of a method 100 personalizing textual content for a user. The method 100 includes steps from 102 to 112.

[0059] The method 100 customizes the textual content on a variety of content platforms, such as websites, articles, news, stories, and the like. The method 100 receives the information regarding the user related details, which enables the method 100 to reduce complexity of the textual content in order to increase readability of the users. Here, the user refers to an individual, which is reading the textual content.

[0060] At step 102, the method 100 includes obtaining a user-specific information about preferences and reading-related behavior of a user by a user metering module of an endpoint device. The user-specific information refers to the data or details that are specific to an individual user or person reading the textual content. The endpoint device refers to a hardware interface on which the user reads the textual content. Examples of the endpoint device may include, but not limited to a mobile phone, a laptop, a computer, and the like. The user metering module is configured to receive the user specific information from the user operating the endpoint device. In accordance with an embodiment, the user-specific information includes one or more of a language that the user speaks, geographic area where the user is based, browsing history of the user, an education level, an expertise domain, and a part of thesaurus of the user. The userspecific information enables to easily customize the textual content. In an implementation, the user metering module is configured to collect dynamic information about reading patterns and related behavior of the user, such as time spent by the user on a page having textual content of specific characteristics (language of the textual content, font size of the textual content, control layout, topics, and the like).

[0061] At step 104, the method 100 includes determining a default comfort readability score, CRDS, of the user by a CRDS calculator of the endpoint device based on the user-specific information. The default CRDS refers to a metric used to assess the complexity of the textual content. In an implementation, the default CRDS is anonymized or non-private, that is, the default CRDS value is not linked to any specific user’s identity or personal information. By ensuring that the default CRDS is anonymized, the method 100 involves providing the personalized content adaptation without compromising the user's personal information or violating the user’s privacy rights. The word “comfort” in the context of CRDS indicates that the magnitude of CRDS provides extent up to which the user is comfortable while reading the textual content. More the magnitude of CRDS, more is the readability of the textual content. In an implementation, the CRDS calculator collects user’s metrics and locally available preferences, such as languages, geographic area, browsing history and the like and matches them with similar text parameters. The degree of similarity between the user-specific information and the text parameters is translated into the CRDS. In accordance with an embodiment, the determining of the default CRDS of the user includes one or more of the CRDS calculator calculating the default CRDS based on a user grade specified by the user, the CRDS calculator calculating the default CRDS based on the userspecific information, and the CRDS calculator calculating the default CRDS dynamically using an information about a service provider the user is going to request for a textual content and the user-specific information. In an implementation, the user grade refers to a numerical value indicating a self-assessment or indication made by the user regarding corresponding reading level or preferences. In another implementation, the user grade refers to a grade in education required of the user for sufficient understanding of the textual content. In an implementation, the CRDS calculator uses the user-specific information about the user's reading habits, preferences, and behavior, which is received by the user metering module. By analyzing the user-specific information, the CRDS calculator calculates the default CRDS that aligns with the user's typical reading preferences. In yet another implementation, the CRDS calculator considers factors related to the service provider, that is, the content provider, such as the type of content usually offered by the content provider, readability standards and the like. By combining the information of the content provider with the user-specific information, the CRDS calculator arrives at a default CRDS that matches both the user's preferences and the content provider's characteristics.

[0062] At step 106, the method 100 includes sending, by the endpoint device to a content provider, a first content request to request a textual content. The first content request includes the default CRDS of the user embedded thereto by a CRDS encoder of the endpoint device. The content provider is a platform or service that offers various types of content for users to access. Examples of the content provider may include, but are not limited to, online encyclopedias, scientific journals, newspapers, online bookstores, digital libraries, and the like. The first content request includes requirement of the textual content according to the default CRDS of the user. The CRDS encoder is a component or module within the endpoint device that is responsible for embedding the CRDS into the first content request sent to the content provider. The CRDS is included in the first content request is crucial to let the content provider know the user's preferred level of readability and comfort when reading. In accordance with an embodiment, the first content request includes a GET request including a Uniform Resource Locator, URL, of the requested textual content and the default CRDS of the user appended to the URL. In an implementation, the GET request is a method of retrieving the requested textual content from the content provider. The CRDS encoder appends the URL of the requested textual content and the default CRDS calculated by the CRDS calculator to create the first content request.

[0063] At step 108, the method 100 includes calculating a readability score, RDS, of the requested textual content by the content provider. The RDS is a numerical representation of the complexity, readability, and difficulty level of the requested textual content. In an implementation, the RDS is based on factors, such as popularity of the words in the requested textual content, difficulty of the words and sentences in the requested textual content, complexity of a syntax, a font type and size, lines and character spacing in the requested textual content.

[0064] At step 110, the method 100 includes adapting the requested textual content by the content provider to correspond to the default CRDS, if the RD S of the requested textual content does not match the default CRDS of the user. After calculating the RDS, the method 100 includes comparing the RDS calculated by the content provider (i.e., server of the content provider) and the default CRDS calculated by the CRDS calculator. If the default CRDS of the user matches with the RDS of the requested textual content, then the textual content provided by the content provider is suitable as per the user-specific information or the user grade. Otherwise, the content provider changes or customizes the requested textual content to match with the default CRDS of the user. In an example, the adjustments made to the requested textual content may be of nature, such as simplifying language, formatting of the textual content, content replacement and the like. By making such adjustments, the method 100 aims to deliver a version of the requested textual content that aligns with the user's comfort level and readability preferences. In an implementation, the exchange of textual content takes place between the endpoint device and the server of the content provider. The default CRDS is specific to the user and the RDS is specific for the requested textual content.

[0065] At step 112, the method 100 includes providing the endpoint device with the textual content adapted to the default CRDS by the content provider together with characteristics of the requested textual content available for the content provider. After receiving the value of default CRDS calculated by the CRDS calculator, the method 100 involves transmitting the default CRDS to the content provider and the content provider adapts or modifies the requested textual content based on the default CRDS. Further, the adapted textual content is transmitted to the endpoint device where user can access the adapted textual content. In an implementation, the characteristics of the requested textual content includes specific details or metadata about the requested textual content that the content provider can access and utilize when processing the requested textual content. The adaptation of the requested textual content takes place until a final text output consistent with the default CRDS of the user is displayed on the endpoint device.

[0066] In accordance with an embodiment, the characteristics of the requested textual content provided by the content provider includes a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content. The maximum available RD S represents the highest level of readability difficulty that the textual content can potentially possess. Further, the server of the content provider specifies the original language in which the textual content is written. By offering the characteristics of the requested textual content, the content provider enhances the user's reading experience and empowers the users to make informed decisions about the requested textual content.

[0067] In accordance with an embodiment, the method 100 further includes updating the default CRDS of the user by the CRDS calculator of the endpoint device based on an update of the userspecific information. The user-specific information changes with change in the user operating the endpoint device. Therefore, when a new user operates the endpoint device, the method 100 involves calculating the default CRDS of the new user and adapt the textual content based on the updated default CRDS.

[0068] In accordance with an embodiment, the default CRDS includes one or more typed values of the user comfort readability score according to one of readability metrics including Flesch-Kincaid Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score. The Flesch- Kincaid Reading Ease rates the readability of text on a scale from 0 to 100. Higher scores indicate easier readability, while lower scores indicate more challenging content. Further, the Flesch-Kincaid Grade Level estimates the United States, US, grade level required to understand the textual content. Furthermore, the Gunning Fog Index calculates the years of formal education required to understand the textual content. In addition, the Coleman-Liau Index uses the number of characters, words, and sentences in the textual content to estimate the grade level. The SMOG Index measures the readability based on the number of words with three or more syllables in a sample of text. The Automated Readability Index uses the average number of characters per word and words per sentence to estimate the text's grade level. The New Dale Chall Readability Formula considers the number of difficult words in a text and estimates the grade level needed to understand the textual content. Further, the Dale Chall Score method counts difficult words in the textual content to assess readability. The Spache Readability Score is specifically designed for children's books and measures the grade level needed to comprehend the text. In accordance with such embodiment, each typed value of the user comfort readability score includes a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value. The readability metric ID is indicative of type of readability metric used for calculating the RDS. The weight of the readability score is indicative of importance or significance of that particular score in the overall calculation of the CRDS. For example, the CRDS may be calculated by two readability matrices, such as the Dale Chall Score method and the Gunning-Fog method. The Dale Chall Score method may be assigned an ID as “NDC”, whereas the Gunning-Fog method may be assigned an ID as “GFG”. Further, the readability score calculated by the Dale Chall Score method may be 8.2, whereas the readability score calculated by the Gunning-Fog method may be 17.6. Further, the weight of the readability score calculated by the Dale Chall Score method may be 2.5, whereas by the Gunning-Fog method may be 3. Therefore, the default CRDS score of the user may be given as {NDC = 8.2 [2.5], GFG = 17.6 [3]}.

[0069] In accordance with an embodiment, the method 100 further includes calculating an adjusted CRDS of the user for the requested textual content by the CRDS calculator of the endpoint device based on a comparison of the characteristics of the requested textual content received from the content provider and related characteristics of the user as per the user-specific information. The adjusted CRDS of the user refers to a modified or adapted CRDS of the user for the requested textual content. The CRDS is calculated based on the user-specific information, whereas the adapted CRDS is calculated based on the readability of the user specific to the requested textual content. During comparison, the CRDS calculator identifies the similarities or differences between the characteristics of the requested textual content and the user-specific information. For example, if the topics covered in the requested content match the user's interests as per the user-specific information, then the CRDS calculator may adjust the CRDS slightly higher to obtain the adjusted CRDS, indicating that the user is likely to find the requested textual content comfortable to read. The adjusted CRDS indicates how well the requested textual content aligns with the user's reading preferences and comfort level. In accordance with such embodiment, the method 100 includes sending, by the endpoint device to the content provider, a second content request to request the textual content, the second content request includes the adjusted CRDS of the user embedded thereto by the CRDS encoder. Similar to the first content request, the second content request based on the adjusted CRDS is transmitted to the content provider. The second content request includes request for a textual content, which is in accordance with the adjusted CRDS. The CRDS encoder takes the calculated CRDS and integrates into the second content request in a specific format or structure for the content provider. Further, in such embodiment, the method 100 includes adapting the requested textual content by the content provider to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user. The method 100 involves comparing the adjusted CRDS with the RDS of the textual content in the second content request and customize the textual content so that the adjusted CRDS matches with the RDS. Further, in such embodiment, the method 100 involves providing the endpoint device with the textual content adapted to the adjusted CRDS by the content provider. After adapting the textual content to match the RDS, the method 100 involves transmitting the adapted textual content as per the second content request to the endpoint device and the endpoint device displays the adapted textual content to the respective user.

[0070] In accordance with an embodiment, the second content request includes a GET request including the URL of the requested textual content and the adjusted CRDS of the user appended to the URL. Moreover, the adjusted CRDS has the same format as the default CRDS. In an example, the format of the second content request may be GET(“URL”, “CRDS”). The format of the adjusted CRDS is in the form of [{Evaluator Type ID, Required Score Value, [Weight]}, . . . ], where the “evaluator type ID” indicates the type of readability matric implanted to calculate the RDS, the “required score value” indicates the value of the RD S and the “weight” indicates the weightage of the RDS.

[0071] In accordance with an embodiment, the calculating of the adjusted CRDS of the user includes the CRDS calculator multiplying the default CRDS of the user by a comprehension score, CS. The CS is a function of a similarity degree between the characteristics of the requested textual content and the related characteristics of the user. Further, the CS function is being defined so that the CS is above one if the user is more capable to comprehend the requested textual content and below one if the user is less capable to comprehend the requested textual content. In an implementation, the CS measures the likelihood of the user’s success in recovery of a text meaning and inferring the correct conclusions from the text. The adjusted CRDS is calculated by following equation (1) as:

[0072] CRDS = CS * RDS (1)

[0073] Wherein, the CS is the comprehension score and the RDS is the readability score. For users having more capability of comprehending the textual content, the value of CS is greater than 1, whereas for users having less compatibility, the value of CS is lesser than 1. The CS varies as per contextual factors of the user and the textual content. For example, for the textual content in native language of the user, the CS may be 1.5, whereas for foreign language, the value of CS may be 0.5, as the user is less capable of comprehending the textual content in foreign language than that of the native language. Similarly, if the textual content is related to the domain of expertise of the user (as per the user-specific information), then in such case, the value of CS may be more than 1. In another example, the textual content having frequent or commonly used words may increase the value of CS, whereas the textual content having rare or non-frequent words may reduce the value of CS.

[0074] In accordance with an embodiment, the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content and the related characteristics of the user. By using the one or more distance measurements, the value of the CS indicates how close or far apart the characteristics of the requested textual content and the related characteristics of the user are from each other. A smaller distance indicates higher similarity, while a larger distance suggests more dissimilarity. By calculating the percentage of matched topics, the value of the CS indicates the number of topics from the requested textual content align with the user's interests or preferred topics. In such embodiment, the value of CS is calculated based on the following equation (2)

[0075] CS = fMatch ({uFactor_l, . . . ,uFactor_N}, {tFactor_l, . . . ,tFactor_M}) (2) whereas, u factors (i.e., uFactor l, ....uFactor N) are related characteristics of the user, such as expertise domain, thesaurus and the like and the t factors (i.e., tFactor l, . . . .tFactor M) are characteristics of the requested textual content, such as covered expertise domains, use of the appropriate words and the like. The term fMatch indicates the measure of similarity between the characteristics of the requested textual content and the related characteristics of the user, that is, the one or more distance measurements, the percentage of matched topics, and the comparison of weighed word frequencies. In accordance with an embodiment, the characteristics of the textual content are embedded into a Hypertext Markup Language, HTML, page element of a web page with the textual content. In an example, the content provider may be a website and the related characteristics of the textual content may be given in the HTML form as follows:

[0076] < / TextABC>

[0077] <RDS ETP=SPH>

[0078] Recent = 8.4, MRDS = 22.8

[0079] < / RDS>,

[0080] <EDM>

[0081] Photo, Travel, Scotland

[0082] < / EDM>,

[0083] < / TextABC>

[0084] From the HTML element mentioned above, the textual content on the webpage meets 8.4 grade readability by Spache with maximum RDS as 22.8. Further, the textual content on the webpage is associated with the expertise such as “Photo”, “Travel” and “Scotland”.

[0085] In an implementation, a page reading time is a factor while determining the CS of the textual content provided by the content provider. The page reading time refers to the time spent by a definite user on a definite webpage having the textual content. The server of the content provider detects the average time spent by users belonging to a definite range of CRDS values. Based on the data collected by the server, the CRDS calculator determines the value of CS as given by following equation (3)

[0086] CS= (AverageTimeOnPage / UserTimeOnPage) * (LanguageWeight(LanguagePreference))

[0087] Whereas the term “AverageTimeOnPage” indicates the average time spent by the users on the webpage, “UserTimeOnPage” indicates the time spent by a particular user on the webpage, “LanguagePreference” indicates the language preference of the user and the “LanguageWeighf ’ indicates weightage of the language preferred by the user (through the user-specific information).

[0088] The method 100 involves determining the default CRDS of the user and adapting the textual content provided by the content provider in accordance with the default CRDS, which minimize errors in the adaptation of the textual content and personalize the textual content based on the user's skills. The method 100 involves dynamic CRDS adjustment based evaluation of the matching degree between the characteristics of the textual content and the user, which ensures that the user receives the textual content that aligns with corresponding reading comfort level and interests, thereby making the reading experience more enjoyable and engaging. The structure of negotiation mechanisms, such as the exchange of CRDS used in the method 100 between the endpoint device and the content provider maintains privacy of the user. The method 100 helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0089] FIG. 2 is a block diagram that depicts an apparatus for personalizing textual content in an endpoint device, in accordance with an embodiment of the present disclosure. With reference to FIG. 2, there is shown a block diagram 200 that depicts an apparatus 202 for personalizing textual content in an endpoint device 204.

[0090] The apparatus 202 includes a user metering module 206 configured for obtaining a user-specific information about preferences and reading-related behavior of a user of the endpoint device 204. The endpoint device 204 refers to a hardware interface on which the user reads the textual content. Examples of the endpoint device may include, but not limited to a mobile phone, a laptop, a computer, and the like. The user metering module 206 is configured to receive the user specific information from the user operating the endpoint device. In an implementation, the user-specific information includes one or more of a language that the user speaks, geographic area where the user is based, browsing history of the user, an education level, an expertise domain, and a part of thesaurus of the user. Further, the apparatus 202 includes a comfort readability score, CRDS, calculator 208 configured for determining a default CRDS of the user based on the user-specific information. The default CRDS refers to a metric used to assess the complexity of the textual content. In an implementation, the CRDS calculator collects user’s metrics and locally available preferences, such as languages, geographic area, browsing history and the like and matches them with similar text parameters. The degree of similarity between the user-specific information and the text parameters is translated into the CRDS. In accordance with an embodiment, the CRDS calculator is configured for determining the default CRDS of the user by one or more of: calculating the default CRDS based on a user grade specified by the user, calculating the default CRDS based on the user-specific information, and calculating the default CRDS dynamically using an information about a service provider the user is going to request for a textual content and the user-specific information. In an implementation, the user grade refers to a numerical value indicating a self-assessment or indication made by the user regarding corresponding reading level or preferences, the CRDS calculator uses the userspecific information about the user's reading habits, preferences, and behavior, which is received by the user metering module. By analyzing the user-specific information, the CRDS calculator calculates a default CRDS that aligns with the user's typical reading preferences.

[0091] The apparatus 202 further includes a CRDS encoder 210 configured for embedding the default CRDS of the user into a first content request that is sent by the endpoint device 204 to a content provider 212 to request a textual content. The content provider 212 is a server that acts as platform or service offering various types of content for users to access. Examples of the content provider may include, but are not limited to, online encyclopedias, scientific journals, newspapers, online bookstores, digital libraries, and the like. The first content request includes requirement of the textual content according to the default CRDS of the user. The CRDS encoder 210 is responsible for embedding the CRDS into the first content request sent to the content provider.

[0092] In accordance with an embodiment, the first content request includes a GET request including a Uniform Resource Locator, URL, of the requested textual content and the CRDS encoder 210 is configured for appending the default CRDS of the user to the URL in the GET request. In an implementation, the GET request is a method of retrieving the requested textual content from the content provider 212. The CRDS encoder 210 appends the URL of the requested textual content and the default CRDS calculated by the CRDS calculator 208 to create the first content request. In accordance with such embodiment, the default CRDS includes one or more typed values of the user comfort readability score according to one of readability metrics including Flesch-Kincaid Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure Of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score, and each typed value of the user comfort readability score comprises a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value. The Flesch-Kincaid Reading Ease rates the readability of text on a scale from 0 to 100. Higher scores indicate easier readability, while lower scores indicate more challenging content. Further, the Flesch-Kincaid Grade Level estimates the United States, US, grade level required to understand the textual content. Furthermore, the Gunning Fog Index calculates the years of formal education required to understand the textual content. In addition, the Coleman- Liau Index uses the number of characters, words, and sentences in the textual content to estimate the grade level. The SMOG Index measures the readability based on the number of words with three or more syllables in a sample of text. The Automated Readability Index uses the average number of characters per word and words per sentence to estimate the text's grade level. The New Dale Chall Readability Formula considers the number of difficult words in a text and estimates the grade level needed to understand the textual content. Further, the Dale Chall Score method counts difficult words in the textual content to assess readability. The Spache Readability Score is specifically designed for children's books and measures the grade level needed to comprehend the text.

[0093] In operation, the endpoint device 204 is configured for receiving the textual content adapted to the default CRDS from the content provider 212 together with characteristics of the requested textual content available for the content provider 212. After receiving the value of default CRDS calculated by the CRDS calculator 208, the apparatus 202 is configured to transmit the default CRDS to the content provider 212 and the content provider 212 adapts or modifies the requested textual content based on the default CRDS. Further, the adapted textual content is transmitted to the endpoint device 204 where user can access the adapted textual content. In an implementation, the characteristics of the requested textual content includes specific details or metadata about the requested textual content that the content provider 212 can access and utilize when processing the requested textual content.

[0094] In accordance with an embodiment, the CRDS calculator 208 is configured for calculating an adjusted CRDS of the user for the requested textual content based on a comparison of the characteristics of the requested textual content received from the content provider 212 and related characteristics of the user as per the user-specific information and updating the default CRDS of the user based on an update of the user-specific information. The adjusted CRDS of the user refers to a modified or adapted CRDS of the user for the requested textual content. The CRDS is calculated based on the user-specific information, whereas the adjusted CRDS is calculated based on the readability of the user specific to the requested textual content. During comparison, the CRDS calculator 208 identifies the similarities or differences between the characteristics of the requested textual content and the user-specific information. For example, if the topics covered in the requested content match the user's interests as per the user-specific information, then the CRDS calculator 208 may adjust the CRDS slightly higher to obtain the adjusted CRDS, indicating that the user is likely to find the requested textual content comfortable to read.

[0095] Further, the endpoint device 204 is configured for sending a second content request to the content provider to request the textual content if the adjusted CRDS differs from the default CRDS by a predefined threshold. Moreover, the CRDS encoder is configured for embedding the adjusted CRDS of the user into the second content request that is sent by the endpoint device to the content provider, and the endpoint device 204 is configured for receiving the textual content adapted to the adjusted CRDS from the content provider. If the adjusted CRDS of the user differs from the default CRDS by the predefined threshold, the endpoint device transmits the second content request to the content provider 212. In an implementation, the second content request is indicative of a condition where the user's preferences have not been fully met by the textual content provided by the content provider 212 during the first content request and the user may prefer content better aligned with corresponding adjusted CRDS.

[0096] In accordance with an embodiment, the CRDS calculator is configured for calculating the adjusted CRDS of the user by multiplying the default CRDS of the user by a comprehension score, CS, wherein the CS is a function of a similarity degree between the characteristics of the requested textual content and the related characteristics of the user, the CS function is defined so that the CS is above one if the user is more capable to comprehend the requested textual content and below one if the user is less capable to comprehend the requested textual content, and the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content and the related characteristics of the user. In an implementation, the CS measures the likelihood of the user’s success in recovery of a text meaning and inferring the correct conclusions from the text, more distance measurements, the value of the CS indicates how close or far apart the characteristics of the requested textual content and the related characteristics of the user are from each other. A smaller distance indicates higher similarity, while a larger distance suggests more dissimilarity. By calculating the percentage of matched topics, the value of the CS indicates the number of topics from the requested textual content align with the user's interests or preferred topics.

[0097] In accordance with an embodiment, the second content request includes a GET request including the URL of the requested textual content, and the CRDS encoder 210 is configured for appending the adjusted CRDS of the user to the URL in the GET request. Moreover, the adjusted CRDS has the same format as the default CRDS. The GET request is a method of retrieving the requested textual content from the content provider 212. The CRDS encoder 210 appends the URL of the requested textual content and the adjusted CRDS calculated by the CRDS calculator 208 to create the second content request.

[0098] The apparatus 202 determines the default CRDS of the user and adapting the textual content provided by the content provider 212 in accordance with the default CRDS, which minimize errors in the adaptation of the textual content and personalize the textual content based on the user's skills. The apparatus 202 is configured to perform dynamic CRDS adjustment based evaluation of the matching degree between the characteristics of the textual content and the user, which ensures that the user receives the textual content that aligns with corresponding reading comfort level and interests, thereby making the reading experience more enjoyable and engaging. The structure of negotiation mechanisms, such as the exchange of CRDS used in the apparatus 202 between the endpoint device 204 and the content provider 212 maintains privacy of the user. The apparatus 202 helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0099] FIG. 3 is a block diagram that depicts an apparatus for personalizing textual content on a content provider side, in accordance with an embodiment of the present disclosure. With reference to FIG. 3, there is shown a block diagram 300 that depicts an apparatus 302 for personalizing textual content on a content provider side.

[0100] The apparatus 302 is associated with a content provider 306 and is configured to personalize the textual content on the content provider side. The content provider 306 is a server, which is configured to transmit the textual content to an endpoint device 308 and a user access the textual content on the endpoint device 308. The endpoint device 308 is a hardware interface between the user and the endpoint device 308, which is configured to display the textual content to the user. Examples of the endpoint device 308 may include, but are not limited to, a mobile phone, a laptop, a computer, a tablet device, and the like.

[0101] The apparatus 302 includes a readability score, RDS, evaluator 304 configured for calculating an RDS of a textual content in reply to receiving by a content provider 212 a first content request from the endpoint device 308, the first content request requesting the textual content and embedding a default CRDS of a user of the endpoint device 308. The RDS refers to a numerical value that indicates the readability or legibility of the textual content for users. In operation, the user operating the endpoint device 308 enters a user specific information into the endpoint device 308 and based on the user specific information, the apparatus 302 enables the endpoint device 308 to generate the default CRDS. The default CRDS is a score calculated by the apparatus 302, which indicates the extent of comfort for a particular user reading the textual content. Further, the apparatus 302 operates the endpoint device to generate the first content request. The first content request further includes the default CRDS calculated by the apparatus 302. The first content request involves request for a textual content from the content provider according to the default CRDS. After receiving the first content request, the content provider 306 transmits the textual content to the apparatus 302. Based on the textual content received from the content provider 306, the RDS evaluator 304 calculates the RDS. In an implementation, the RDS is an averaged value or a vector of typed value. The apparatus 302 further includes an adaptive transformer 310 configured for adapting the requested textual content to correspond to the default CRDS, if the RDS of the requested textual content does not match the default CRDS of the user. The adaptive transformer 310 is configured to customize or change the requested textual content based on the default CRDS. In an implementation, the adaptive transformer 310 is configured to implement multiple text transformation techniques or querying external transformation service.

[0102] In operation, the content provider 306 is configured for providing the endpoint device 308 with the textual content adapted to the default CRDS together with characteristics of the requested textual content that includes a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content. The maximum available RDS represents the highest level of readability difficulty that the textual content can potentially possess. Further, the content provider 306 specifies the original language in which the textual content is written. By offering the characteristics of the requested textual content, the content provider 306 enhances the user's reading experience and empowers the users to make informed decisions about the requested textual content.

[0103] In accordance with an embodiment, the content provider 306 is configured for receiving a second content request for the textual content from the endpoint device 308, the second content request embedding an adjusted CRDS of the user of the endpoint device 308. The second content request is generated by the endpoint device 308 if there is significant difference between the default CRDS and the RDS. Further, the apparatus 302 enables the endpoint device 308 to calculate the adjusted CRDS and transmits the second content request to the adaptive transformer 310. In such embodiment, the adaptive transformer 310 is configured for adapting the requested textual content to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user, and the content provider 306 is further configured for providing the endpoint device 308 with the textual content adapted to the adjusted CRDS of the user. In an implementation, the adaptive transformer 310 is configured to compare the adjusted CRDS and the RDS and adapt the textual content until the adjusted CRDS matches with the RDS. In accordance with an embodiment, the RDS evaluator is further configured for embedding characteristics of the textual content into a Hypertext Markup Language, HTML, page element of a web page with the textual content.

[0104] The RDS evaluator in the apparatus 302 determines the RDS of the textual content and adapts the textual content to match with the default CRDS of the user, which minimizes errors in the adaptation of the textual content and personalize the textual content based on the user's skills. The adaptive transformer 310 in the apparatus 302 is configured to perform dynamic CRDS adjustment based on the evaluation of the degree of similarity between the characteristics of the textual content and the user, which ensures that the user receives the textual content that aligns with corresponding reading comfort level and interests, thereby making the reading experience more enjoyable and engaging. The structure of negotiation mechanisms, such as the exchange of CRDS used in the apparatus 302 between the endpoint device 308 and the content provider 306 maintains privacy of the user. The apparatus 302 helps the content providers to have information related to the growth of the targeted audiences and user’s stickiness.

[0105] FIG. 4 is a diagram illustrating an exemplary scenario of operations of an apparatus for personalizing textual content in an endpoint device and an apparatus for personalizing textual content on a content provider side, in accordance with an embodiment of the present disclosure. With reference to FIG. 4, there is shown a diagram 400 illustrating an exemplary scenario of operations of the apparatus 202 (of FIG. 2) for personalizing textual content in the endpoint device 204 (of FIG. 2) and the apparatus 302 (of FIG. 3) for personalizing textual content on a content provider side.

[0106] The apparatus 202 is configured to determine the default CRDS related to a user 408 on the endpoint side, whereas the apparatus 302 is configured to adapt the textual content based on the default CRDS. In operation of the apparatus 202, the user metering module 206 is configured to receive a user-specific information 406 from the user 408, such as language preferences, locality of the user 408, browsing history of the user 408. Further, the user metering module 206 transmits the user-specific information 406 to the CRDS calculator 208. The CRDS calculator 208 is configured to determine the default CRDS and generates the first content request. Further, the CRDS encoder 210 appends the default CRDS with the URL of a webpage (i.e., requested textual content) to which the user is browsing on the endpoint device 204. In such exemplary scenario, the CRDS calculator 208 calculates the default CRDS as 7. Thus, the first content request is in the form as “URL, CRDS = 7”. Further, the first content request is transmitted to the content provider 212. After receiving the first content request, the apparatus 302 is operated. The apparatus 302 includes an adaptive content transformer 404, which includes the RDS evaluator 304 and the adaptive transformer 310. During operation of the apparatus 302, the RDS evaluator 304 is configured to calculate the RDS. In such exemplary scenario, the RDS evaluator 304 calculates the RDS as 6.8. Further, based on the difference between the RDS and the default CRDS (i.e., 7 - 6.8 = 0.2), the adaptive transformer 310 is configured to adapt or customize the textual content on the webpage of which URL is included in the first content request to match with the default CRDS. After customizing the textual content, the apparatus 302 enables the content provider 212 to append a characteristics of the requested textual content 402 and the requested textual content. In such scenario, the characteristics of the requested textual content 402 may include a metrics having a maximum RDS value, such as 16.3, language of the requested textual content, such as “British English”, the topics covered in the textual content and a set of words from the textual content, such as “Travel, Fishing, Scotland”. The process of adapting the requested textual content is continued until the value of RDS becomes equal to the value of the default CRDS.

[0107] FIG. 5 is a diagram illustrating an exemplary scenario of negotiation capabilities and CRDS adjustments between an endpoint device and an adaptive Tenderer, in accordance with an embodiment of the present disclosure. With reference to FIG. 5, there is shown a diagram illustrating an exemplary scenario of negotiation capabilities and CRDS adjustments between an endpoint device 204 and an adaptive Tenderer 506.

[0108] In the exemplary scenario, the user 408 is operating the endpoint device 204 and the RDS evaluator 304 is configured with the endpoint device as shown in FIG. 5. In an implementation, the adaptive Tenderer 506 is a server associated with the content provider, which is configured to retrieve textual content from the internet based on the default CRDS determined by the endpoint device and the RDS determined by the RDS evaluator 304. The endpoint device determines the default CRDS based on the user-specific information received from the user 408. In an implementation, the user-specific information is determined based on the browsing history of the user or by interacting with third party services (such as DNS). Further, the endpoint device generates a first content request 502. The first content request 502 is in the form of GET request, which includes the URL of the webpage displayed on the endpoint device 204 and the default CRDS calculated by the endpoint device 204. Further, the adaptive Tenderer 506 provides the RDS calculated by the adaptive Tenderer 506 along with the characteristics of the requested textual content. In such example, the value of RDS may be 7, and the characteristics of the requested textual content may include maximum RDS as 22.6, topics covered by the requested textual content, such as photo, travel, and the like. During determination of the default CRDS, the endpoint device 204 is configured to determine the CS of the user 408. In such an example, the CS may be determined using parameters that define the ability of comprehension associated with the user 408. For example, the user 408 may be able to comprehend textual content written in the native language of the user 408, the user 408 may have frequently visited websites having photos and travel related information and the like. Based on such parameters, the value of the CS calculated by the endpoint device 204 may be 15.9. Further, the RDS evaluator 304 continuously determines the RDS of the requested textual content and generates a second content request 504, which includes information such as URL of the webpage and the value of RD S (in an example, may be 16). Further, the RDS evaluator 304 is configured to transmit the second content request 504 to the adaptive Tenderer 506 and the adaptive Tenderer 506 adapts the requested textual content as per the second content request 504. Further, the RDS evaluator 304 determines the RDS of the requested textual content as per the second content request. The adaptive Tenderer 506 selects the textual content from multiple pre-ready suitable contents based on the RDS. The negotiation and CRDS adjustments take place between the endpoint device 204 and the adaptive Tenderer 506 until the RDS of the requested textual content matches with the default CRDS of the user 408.

[0109] FIG. 6 is a diagram illustrating an adjustment flow of a default CRDS between a browser on an endpoint device and a server associated with a content provider, in accordance with an embodiment of the present disclosure. With reference to FIG. 6, there is shown a diagram 600 illustrating an adjustment flow of a default CRDS between a browser 602 on the endpoint device 204 (of FIG. 2) and a server 604 associated with a content provider 212. With reference to FIG. 6, there is shown the adjustment flow for the first content request 502 and the second content request 504.

[0110] In operation (for the first content request 502 based on a fair default CRDS), the user 408 is reading textual content on the browser 602 displayed on the endpoint device 204 (of FIG. 2). The browser 602 includes a Tenderer 606, which retrieves URL of the webpage (on which the user 408 is reading the textual content) using the GET function, which is represented as GET(URL). Further, the CRDS calculator 208 is configured to determine the default CRDS and the CRDS encoder 210 is configured to append the URL of the webpage (i.e., GET (URL)) with the default CRDS to generate the first content request 502 (which is represented as GET (URL, DEFAULT CRDS)). Further, the first content request 502 is transmitted to the server 604. The server 604 includes a CRDS retriever 608, which is configured to retrieve the default CRDS from the first content request 502. Furthermore, the server 604 includes the adaptive Tenderer 506, which compares the default CRDS with the RDS calculated by the RDS evaluator 304 in the server 604. If the value of the RDS is less than or equal to the default CRDS, then the adaptive Tenderer 506 is configured to simplify the requested textual content in the first content request 502 until the value of RDS becomes greater than or equal to the default CRDS. In case, the value of RDS becomes greater than or equal to the default CRDS, the adaptive render 506 takes the details of the URL of the webpage with the adapted or simplified textual content (which is represented as “GETDETAILS (URL)”) and sends a first textual content 610 from the content provider 212 along with the characteristics of the adapted textual content, such as the maximum RDS, language of the textual content and the like to the CRDS calculator 208. Further, the CRDS calculator 208 recalculates the adjusted CRDS based on the difference between the RDS of the first textual content 610 and the default CRDS. If there is significant difference between the RDS of the first textual content 610 and the adjusted CRDS, the endpoint device 204 generates the second content request 504, which includes the URL of the webpage appended with the adjusted CRDS. Further, the server 604 retrieves a second textual content 612 based on the second content request 504 and the RDS evaluator 304 calculates the RDS for the corresponding textual content. The process of simplifying the textual content, calculating the RDS and comparing with the adjusted CRDS continues till a final text output with equal RDS and the adjusted CRDS values.

[0111] FIG. 7 is a diagram illustrating an apparatus for personalizing textual content in an endpoint device, in accordance with a different embodiment of the present disclosure. With reference to FIG. 7, there is shown a diagram illustrating an apparatus 700 for personalizing textual content in the endpoint device 204, which includes a transformation service module 708, which is configured to adapt the textual content. The operation of the apparatus 700 involves steps from 702 to 706.

[0112] The transformation service module 708 refers to a cloud server, which is configured to transform or customize the requested textual content requested by the endpoint device 204. In operation, the user 408 enters the user-specific information into the endpoint device 204. Further, at step 702, the endpoint device 204 configures or calculates a default CRDS based on the user-specific information. Further, the endpoint device 204 generates the first content request 502 (of FIG. 5). During the generation of the first content request 502, at step 704, the CRDS encoder 210 (of FIG. 2) is configured to specify the default CRDS as a part of the URL of a webpage on which the user 408 reads the textual content. The CRDS encoder 210 appends the URL of the webpage with the default CRDS to the transformation service module 708. The transformation service module 708 is configured to adapt a textual content provided by the content provider 212 (of FIG. 2) based on the default CRDS. During adapting the textual content, at step 706, the transformation service module 708 is configured to pick a pre-ready suitable content as per the default CRDS of the user 408 and transmits the pre-ready suitable content to the endpoint device 204. The endpoint device 204 displays the pre-ready suitable content to the user 408. In an implementation, the apparatus 700 involves use of pre-ready adapted version instead of run-time adaptation. In an implementation, the apparatus 700 is supported by the CDN cache and the adaptation of the textual content is performed once (i.e., on first content request) to minimize overload and related fees.

[0113] 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 personalizing textual content for a user (408), the method (100) comprising: obtaining a user-specific information (406) about preferences and reading-related behavior of a user (408) by a user metering module (206) of an endpoint device (204), determining a default comfort readability score, CRDS, of the user (408) by a CRDS calculator (208) of the endpoint device (204) based on the user-specific information (406), sending, by the endpoint device (204) to a content provider (212), a first content request (502) to request a textual content, the first content request (502) comprising the default CRDS of the user (408) embedded thereto by a CRDS encoder (210) of the endpoint device (204), calculating a readability score, RDS, of the requested textual content by the content provider (212), adapting the requested textual content by the content provider (212) to correspond to the default CRDS, if the RDS of the requested textual content does not match the default CRDS of the user (408), and providing the endpoint device (204) with the textual content adapted to the default CRDS by the content provider (212) together with characteristics of the requested textual content (402) available for the content provider (212).

2. The method (100) of claim 1, wherein the determining of the default CRDS of the user (408) comprises one or more of: the CRDS calculator (208) calculating the default CRDS based on a user grade specified by the user (408), the CRDS calculator (208) calculating the default CRDS based on the user-specific information (406), and the CRDS calculator (208) calculating the default CRDS dynamically using an information about a service provider the user (408) is going to request for a textual content and the user-specific information (406).

3. The method (100) of claim 1 or 2, further comprising: calculating an adjusted CRDS of the user (408) for the requested textual content by the CRDS calculator (208) of the endpoint device (204) based on a comparison of thecharacteristics of the requested textual content (402) received from the content provider (212) and related characteristics of the user (408) as per the user-specific information (406), sending, by the endpoint device (204) to the content provider (212), a second content request (504) to request the textual content, the second content request (504) comprising the adjusted CRDS of the user (408) embedded thereto by the CRDS encoder (210), adapting the requested textual content by the content provider (212) to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user, and providing the endpoint device (204) with the textual content adapted to the adjusted CRDS by the content provider (212).

4. The method (100) of claim 3, wherein the calculating of the adjusted CRDS of the user (408) comprises the CRDS calculator (208) multiplying the default CRDS of the user (408) by a comprehension score, CS, wherein the CS is a function of a similarity degree between the characteristics of the requested textual content (402) and the related characteristics of the user, the CS function being defined so that the CS is above one if the user (408) is more capable to comprehend the requested textual content and below one if the user (408) is less capable to comprehend the requested textual content.

5. The method (100) of claim 4, wherein the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content (402) and the related characteristics of the user.

6. The method (100) of any of claims 1 to 5, wherein the user-specific information (406) comprises one or more of a language that the user (408) speaks, geographic area where the user (408) is based, browsing history of the user, an education level, an expertise domain and a part of thesaurus of the user; and the characteristics of the requested textual content (402) provided by the content provider (212) comprise a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content.

7. The method (100) of any of claims 1 to 6, further comprising updating the default CRDS of the user (408) by the CRDS calculator (208) of the endpoint device (204) based on an update of the user-specific information (406).

8. The method (100) of any of claims 1 to 7, wherein the first content request (502) comprises a GET request including a Uniform Resource Locator, URL, of the requested textual content and the default CRDS of the user (408) appended to the URL, wherein the default CRDS comprises one or more typed values of the user (408) comfort readability score according to one of readability metrics including Flesch-Kincaid Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure Of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score.

9. The method (100) of claim 8, wherein each typed value of the user comfort readability score comprises a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value.

10. The method (100) of claim 8 or 9, wherein the second content request (504) comprises a GET request including the URL of the requested textual content and the adjusted CRDS of the user (408) appended to the URL, wherein the adjusted CRDS has the same format as the default CRDS.

11. The method (100) of any of claims 1 to 10, wherein the characteristics of the textual content are embedded into a Hypertext Markup Language, HTML, page element of a web page with the textual content.

12. An apparatus (202) for personalizing textual content in an endpoint device (204), the apparatus (202) comprising: a user metering module (206) configured for obtaining a user-specific information (406) about preferences and reading-related behavior of a user of the endpoint device (204), a comfort readability score, CRDS, calculator (208) configured for determining a default CRDS of the user (408) based on the user-specific information (406), anda CRDS encoder (210) configured for embedding the default CRDS of the user (408) into a first content request (502) that is sent by the endpoint device (204) to a content provider (212) to request a textual content, where the endpoint device (204) is configured for receiving the textual content adapted to the default CRDS from the content provider (212) together with characteristics of the requested textual content (402) available for the content provider (212).

13. The apparatus (202) of claim 12, wherein the CRDS calculator (208) is configured for determining the default CRDS of the user (408) by one or more of calculating the default CRDS based on a user grade specified by the user, calculating the default CRDS based on the user-specific information (406), and calculating the default CRDS dynamically using an information about a service provider the user (408) is going to request for a textual content and the user-specific information (406).

14. The apparatus (202) of claim 12 or 13, wherein the CRDS calculator (208) is configured for: calculating an adjusted CRDS of the user (408) for the requested textual content based on a comparison of the characteristics of the requested textual content (402) received from the content provider (212) and related characteristics of the user (408) as per the user-specific information (406), and updating the default CRDS of the user (408) based on an update of the userspecific information (406), the endpoint device (204) is configured for sending a second content request (504) to the content provider (212) to request the textual content if the adjusted CRDS differs from the default CRDS by a predefined threshold, the CRDS encoder (210) is configured for embedding the adjusted CRDS of the user (408) into the second content request (504) that is sent by the endpoint device (204) to the content provider (212), and the endpoint device (204) is configured for receiving the textual content adapted to the adjusted CRDS from the content provider (212).

15. The apparatus (202) of claim 14, wherein the CRDS calculator (208) is configured for calculating the adjusted CRDS of the user (408) by multiplying the default CRDS of the user by a comprehension score, CS, wherein the CS is a function of a similarity degree betweenthe characteristics of the requested textual content (402) and the related characteristics of the user, the CS function is defined so that the CS is above one if the user (408) is more capable to comprehend the requested textual content and below one if the user (408) is less capable to comprehend the requested textual content, and the CS function is based on one or more of distance measurements, a percentage of matched topics and a comparison of weighed word frequencies between the characteristics of the requested textual content (402) and the related characteristics of the user.

16. The apparatus (202) of any of claims 12 to 15, wherein the first content request (502) comprises a GET request including a Uniform Resource Locator, URL, of the requested textual content, the CRDS encoder (210) is configured for appending the default CRDS of the user (408) to the URL in the GET request, the default CRDS comprises one or more typed values of the user comfort readability score according to one of readability metrics including Flesch-Kincaid Reading Ease, Flesch- Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Simple Measure Of Gobbledygook, SMOG, Index Score, Automated Readability Index, New Dale Chall Readability Formula, Dale Chall Score and Spache Readability Score, and each typed value of the user comfort readability score comprises a readability metric identifier, ID, a readability score value according to said readability metric and a weight of said readability score value.

17. The apparatus (202) of claim 16, wherein the second content request (504) comprises a GET request including the URL of the requested textual content, and the CRDS encoder (210) is configured for appending the adjusted CRDS of the user (408) to the URL in the GET request, wherein the adjusted CRDS has the same format as the default CRDS.

18. An apparatus (302) for personalizing textual content on a content provider side, the apparatus (302) comprising: a readability score, RDS, evaluator (304) configured for calculating an RDS of a textual content in reply to receiving by a content provider (212) a first content request (502) from an endpoint device (204), the first content request (502) requesting the textual content and embedding a default CRDS of a user (408) of the endpoint device (204), andan adaptive transformer (310) configured for adapting the requested textual content to correspond to the default CRDS, if the RD S of the requested textual content does not match the default CRDS of the user (408), wherein the content provider (212) is configured for providing the endpoint device (204) with the textual content adapted to the default CRDS together with characteristics of the requested textual content (402) that comprise a maximum available RDS of the textual content and one or more of original language of the textual content, topics covered in the textual content and a set of words from the textual content.

19. The apparatus (302) of claim 18, wherein the content provider (212) is configured for receiving a second content request (504) for the textual content from the endpoint device (204), the second content request (504) embedding an adjusted CRDS of the user (408) of the endpoint device (204), the adaptive transformer (310) is configured for adapting the requested textual content to correspond to the adjusted CRDS, if the RDS of the requested textual content does not match the adjusted CRDS of the user, and the content provider (212) is further configured for providing the endpoint device (204) with the textual content adapted to the adjusted CRDS of the user.

20. The apparatus (302) of claim 18 or 19, wherein the RDS evaluator (304) is further configured for embedding characteristics of the textual content into a Hypertext Markup Language, HTML, page element of a web page with the textual content.

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