Abstract:
Content personalized for a user is presented. Particularly, content is personalized and presented to a user in a more cognitive and user-understandable manner to improve the impact and the effectiveness on the user. The system utilizes artificial intelligence to analyze and categorize the content and thereby learns to discover the core concept of the content and any patterns involved. The system also understands the user&#39;s interests by capturing the preferred presentation formats and the user&#39;s past knowledge. The system maps the categorized content and user&#39;s interests and personalizes the content and renders into user preferred presentation type and format. The system supplements the main presentation type with additional related content. The system is capable of continuously monitoring the user activities to understand the effectiveness of the presented content type and formats, and feedback is exploited to continuous improvement of presented content and presentation type and formats.

Description:
BACKGROUND 
       [0001]    1. Technical Field 
         [0002]    The field generally relates to presentation of the network contents and more specifically, to a method and system for presenting the content personalized to the users in more cognitive and effective way to improve the understandability of the presented content and the impact and effectiveness of the presented content to enable the ease of knowledge acquisition for the user. 
         [0003]    2. Description of Related Art 
         [0004]    The fundamental intention of any content is to make the user understand the core subject matter discussed in that content. The Conventional content delivery systems available in the art merely present the content to a user from the pre-categorized content types and based on the user&#39;s feedback on the content type but without any changes in the presentation type or format. Often users find understanding this content more time consuming, difficult to understand. After taking good amount of time to go through the content, and grasping the intent of the content, users may feel the content not so relevant, ineffective and insignificant. 
         [0005]    Especially when the content is in textual format it is more difficult to the user to quickly grasp the contents of the content presented. Many studies have concluded the fact that visual representation of a concept will have more impact on the user and would be easier for the user to get the intent of content presented in much lesser time with more precision. Similarly, when the subject matter is data intensive or contains inherent complex relationships between entities, it would be challenging to intuitively represent it in a textual mode. It would be difficult for the users to discover trends, patterns and establish relationships in this medium. Using more intuitive data visuals like infographics or graphs or content map or a combination of text and graphics would help the user to better make sense of it. Also, to have more insights and draw conclusions out of the content presented, user needs to go through other related content on the concept of interest by finding them manually and then temporarily memorize the inputs gained from the content presented before. 
         [0006]    Conventional content delivery systems present the content data without any changes in the presenting style or format and fail to create the impact of content presented on users. Also, these systems don&#39;t focus on capturing user&#39;s attention and motivation levels. This would in-turn affect the retention and recollection of the concept. As a result, users would not be able to obtain the knowledge contained and intended in the content presented. Also most often the content delivery systems present the content type statically for all users without considering their friendlier presentation types and their past knowledge about the subject matter. 
       SUMMARY 
       [0007]    One of the embodiments of present invention relates to a method for presenting the content personalized for the user. The method includes analyzing and categorizing the content using artificial intelligence techniques to discover the core concept of content and any patterns involved. The method also includes determining the user&#39;s interests, prior knowledge by capturing the user data, preferred content types, presentation type and formats. The information on user&#39;s interests, prior knowledge is stored on a memory. The method further includes mapping the categorized content to user&#39;s interests and prior knowledge to discover the relevant content, from the categorized content, and preferred presentation type and formats. The relevant content discovered is transformed into a preferred presentation type and formats and presented to user in one or more user preferred languages. 
         [0008]    In another embodiment, user&#39;s activities during the presentation are monitored and user&#39;s feedback on the effectiveness of the presented content type and presentation type and formats is collected to update the user&#39;s interests on the memory. Based on the updated user&#39;s interests the most relevant content with a most preferred presentation type and formats is presented. 
         [0009]    One of the embodiments of invention further includes gathering the content related to the core concept of content, presented to the user, from plurality of web sources. The gathered content related to the core concept of content presented to the user is also presented in real time during content presentation. 
         [0010]    Another of the embodiments of present invention relates to a system for presenting the content personalized for the user. The system includes a learning module for analyzing and categorizing the content using artificial intelligence to discover the core concept of the content and any patterns involved. The system further includes a training module for understanding the user&#39;s interests, prior knowledge by capturing the preferred content type, presentation type and formats, user data and storing on a memory. A content processing module maps the categorized content to user&#39;s interests and prior knowledge to discover the relevant content and preferred presentation type and formats. A content processing module also processes the relevant content into a preferred presentation type and formats for presenting to user in one or more user preferred languages. The system further includes an analytics module for monitoring user&#39;s activities during the presentation, collecting user&#39;s feedback on the effectiveness of the presented content type and presentation type and formats and updating the user&#39;s interests on the memory. A presentation module presents the most relevant content with a most preferred presentation type and formats based on the updated user&#39;s interests. 
         [0011]    Another embodiment of present invention further includes a searching module for gathering the content related to core concept of content, presented to the user, from plurality of web sources for further presenting the content related to the core concept of content presented to the user during the content presentation in real time. 
         [0012]    The above presented is the summary of some aspects of the invention and is not an extensive overview of the invention. The summary merely tries to exemplify some of the concepts of the invention in a simplified form. It does not limit the scope or key elements of the invention in any way. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]    These and other features, aspects, and advantages of the present invention will be better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
           [0014]      FIG. 1  illustrates an example of implementation architecture for delivering the personalized content to a user according to an embodiment of the invention. 
           [0015]      FIG. 2  is a flow diagram illustrating a method of delivering the personalized content to a user, according to an embodiment of the invention; 
           [0016]      FIG. 3  illustrates a learning module of the system for delivering the personalized content to a user, according to an embodiment of the invention; 
           [0017]      FIG. 4  illustrates the architecture of the system for delivering the personalized content to a user, according to an embodiment of the invention; 
           [0018]      FIG. 5  illustrates a content transformation module of the system for delivering the personalized content to a user, according to an embodiment of the invention. 
           [0019]      FIG. 6  illustrates a content presentation module of the system for delivering the personalized content to a user, according to an embodiment of the invention; 
           [0020]      FIG. 7  illustrates an analytics module of the system for delivering the personalized content to a user, according to an embodiment of the invention; 
           [0021]      FIG. 8  shows an example view of the personalized content presented to a user, according to an embodiment of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    The following description is the full and informative description of the best method and system presently contemplated for carrying out the present invention which is known to the inventors at the time of filing the patent application. Of course, many modifications and adaptations will be apparent to those skilled in the relevant arts in view of the following description in view of the accompanying drawings and the appended claims. While the system and method described herein are provided with a certain degree of specificity, the present technique may be implemented with either greater or lesser specificity, depending on the needs of the user. Further, some of the features of the present technique may be used to get an advantage without the corresponding use of other features described in the following paragraphs. As such, the present description should be considered as merely illustrative of the principles of the present technique and not in limitation thereof, since the present technique is defined solely by the claims. 
         [0023]      FIG. 1  shows example architecture for implementing the present invention where the user requested content is received from content sources  101 . User requested content such as online contents  102 , online media files  103  is received by the content delivery engine  106  at the server computer  105  through online connectors  108 . The server computer  105  operates in a networked environment which is connected to network via network interface. Services and application programming interfaces (APIs) module  107  of content delivery engine  106  provides the services of cognitive content delivery engine  106 . Hypertext Markup Language (HTML) Renderer Service renders user friendlier presentation type as HTML response. Feeds component provides the functionalities in Really Simple Syndication (RSS) feed format. Software Development Kit (SDK) framework provides API for the cognitive content delivery engine  106 . Content received at content delivery engine  106  is analyzed and categorized by the learning module  109 . Further APIs are provided for the functionalities of the present system including learning module  109 , content processing module  111  and content presentation module  112 . The input devices  120  and output devices  121  form the user interfacing components. The server computer  105  hosting the cognitive content delivery engine  106  is responsible for serving the response to the user requests. The cognitive content delivery engine  106  can also be hosted in cloud to provide this Software-as-Service (SaaS). 
         [0024]    User profile manager  115  and Preferences manager  127  determine if any of the user&#39;s prior knowledge data  123 , training data  124 , and user preferences data  126  is available with the memory  116  or database server  122 . User&#39;s prior knowledge data  123 , Training data  124 , Search index data  125  and User preference data  126  collectively form the knowledge repository. Learning module  109  takes into account also any or all of the user&#39;s prior knowledge  123 , training data  124 , and user preferences data  126  stored at database server  122  or memory  116  with the help of predictive application programming interfaces (APIs) for content categorization. Where the user&#39;s prior knowledge data  123  is not found, user&#39;s knowledge data on the content being processed, among several other aspects associated with user are captured by the training module  110  and stored at the database server  151 . Where the training data  124  is not found, data, related to user, such as user&#39;s education, subject matter expertise, topics of interests, working field, content reading history among many other similar aspects related to user are captured by the training module  110  and stored at the database server  151 . Where the user preferences data  126  is not found, user&#39;s preferences on the content being delivered and the content presentation type and format are captured by the training module  110  and stored at the database server  151 . 
         [0025]    When a user is logging in for the first time, Cognitive Content delivery engine  106  requires the authentication of the user against the user repository. A dedicated Lightweight Directory Access Protocol (LDAP) server  128  is used for storing user profile data  129 . The user profile data  129  includes the specific user&#39;s profile attributes such as user name, phone number and other contact details of user. This user profile data  129  is used by preferences manager for obtaining explicit preferences of user. 
         [0026]    Content processing module  111  receives the content categorized by learning module  109  to perform the mapping step wherein the categorized content is compared and contrasted to the user&#39;s prior knowledge data  123 , training data  124 , and user preferences data  126 . The content personalized based on the mapping of content is further transformed by the content processing module  111  to customize the presentation type and format of content being delivered to the user based on the user preferences data  126 . Personalized and transformed content is further rendered and presented to the user on the user device  130  by the content presentation module  112 . If the user preferences data  126  is not found as in the case of a public user or a guest user scenario, the cognitive content delivery engine  106  identifies the most appropriate presentation type suitable for the concept. 
         [0027]    Various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile device, a global positioning satellite (GPS) device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a personal trusted device, a web appliance, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine can be used as a user device to implement the present system. 
         [0028]    Search module  113  provides online information, relevant to the content being presented to the user, through hyperlinks, videos, articles related to the content subject matter to help user understand the concept better. Search module  113  also provides online information belonging to the same category of the content subject matter to further help the user explore further on the concept of content being presented to the user. Search module also uses social connectors to connect to various social media platforms for providing information, relevant to the content being presented to the user, from popular social media websites. Search module  113  further explores the knowledge resources, on the worldwide web, that include Wikipedia, dbpedia, freebase, Yago, wordnet, quora, yahoo answers, encyclopedia of life, encyclopedia of science, medpedia, howthestuffworks and similar sources. A third party search engine interface may also be included using pluggable extensions to interface with external search engines like Google, Bing to obtain information relevant to the contents being presented to the user. 
         [0029]    Analytics module  114  is provided for actively tracking the user&#39;s behavioral activities during the content presentation to understand user&#39;s behavior. Activity tracking happens both implicitly and explicitly. The implicit tracking of user&#39;s activity monitored comprises at least one among the click events, download events, usability analysis, reading time and scroll data. This helps to find the effectiveness on the user, rendered presentation format, rendered layout of the content being presented to user. Analytics module  114  further updates the user&#39;s interests on the memory  119 . 
         [0030]    As illustrated in  FIG. 2 , a method for presenting the personalized content to a user according to an embodiment of the present invention is designated as  200 . On the request from the user contents received are fed in the step  201  to the learning module  109 . Content fed to the learning module  109  is analyzed and categorized in step  202  by using Natural Language Processing (NLP) techniques. The NLP techniques include at least one of the semantic analysis, spatial analysis, chronological analysis, conceptual analysis, quantitative analysis, pattern analysis, and theme analysis techniques. 
         [0031]    Further, method includes determination step  203  to find the presence of user&#39;s prior knowledge data, and user&#39;s preferences, about the content type and the presentation type and format, with the system according to the present invention. In the event of non-availability of user&#39;s prior knowledge data, system initiates to capture the user&#39;s prior knowledge levels. Similarly in the event of non-availability of user&#39;s preferences, system initiates to capture the user&#39;s preferences about the content type and presentation type and format. In case of a public user or a guest user scenario as in step  215 , the system uses the most appropriate presentation type as the preferred type. In step  205 , the data captured by the system on the user&#39;s knowledge levels and user&#39;s preferences about the content and presentation types and format is stored on the memory for further usage. Data captured by the system on user&#39;s preferences is also shared to the preferences manager for leveraging this data during the content personalization and transformation. 
         [0032]    Content analyzed and categorized in step  202  is further mapped to the user&#39;s knowledge levels and user&#39;s preferences about the content type and presentation type and format, in step  206 , to personalize the content requested by the user. In step  207 , the personalized content is transformed into user preferred presentation type and format and preferred language to make it ready for delivering to the user. In case of public user scenario, content is transformed into best chosen presentation type and format. The content so transformed based on the user&#39;s preferences is further presented to the user at the user device  130  in step  208 . 
         [0033]    According to one embodiment of the invention, the method also includes a step  209  for monitoring the user&#39;s behavior during the personalized content presentation. The user&#39;s activities monitored during presentation comprise the click events, download events, usability analysis, reading time, and scroll data among many other behavioral aspects of user. The data on user&#39;s activities during content presentation is captured and stored on the memory and shared to preferences manager for further usage. Based on the data captured in step  209 , the content being presented to user is further personalized and the most preferred content type is presented to the user in a most preferred presentation type and format to improve the effectiveness of content on the user. 
         [0034]    According to another embodiment of the invention, in step  210 , the method also includes searching for the content relevant to the content personalized and presented to the user in steps  201  to  208 , through various online search techniques and online sources of information using appropriate connectors. In another embodiment, step  211  includes search for the content based on the user&#39;s prior knowledge, preferences regarding the content of interest to the user, previously learnt by the system. In step  212 , the relevant content resulted from the search in step  210  or  211  or both are further presented to the user along with the personalized content being presented to the user. The relevant content can include online links, audio, video information, any further information or sources of content of interest to user. 
         [0035]    According to another embodiment of the invention, in step  213 , the method also can present the user with several analogies for improved content understandability and further enhancement of knowledge levels of the user. Content with concept similar or belonging to the same category of the personalized content being presented is identified by using the core idea and the category of subject matter of personalized content being presented. The analogies are drawn from user&#39;s acquired knowledge or easy-to-understand real world samples. Also, comparison and contrasting of the core concept with any available concept in the knowledge store is presented to the user along with and during the personalized content presentation. 
         [0036]    Functional modules according to the various embodiments of the present invention are now described in detail.  FIG. 3  presents the learning module  300  according to an embodiment of the present invention. Content requested by the user is analyzed and categorized by learning module  300  of the present invention by using the artificial intelligence based techniques. Learning module  300  receives the content feed  301  and employs various NLP techniques to analyze and categorize the content. Semantic analyzer  302  is a component of NLP which processes the words in a given context to extract the “meaning or core concept” of the content feed. Based on the concepts extracted, the content feed  301  is categorized into various concept based categories and concept based categorized content is made available for further mapping process. Spatial analyzer  303  is provided to identify the location related information in the subject matter of content feed  301 . Location related information can include, among many others similar aspects, distance, directions between the locations, maps, geographical identification, and numeral information of location, comparative information, current and past incidents with respect to the locations. Spatial analyzer  303  further analyzes the location information from content feed  301  and helps to render the content into graphical representations, geographical maps, route maps and other appropriate types of representation. 
         [0037]    Chronological Analyzer  304  is provided to identify the sequence of steps, events happening in chronological order, timeline events from the content feed  301 . The events/steps are extracted from the content feed  301  and provided in a well-defined structure which can be extracted by the presentation renderer during content presentation. Further, conceptual Analyzer  305  extracts all key concepts and sub-concepts from the subject matter of content feed  301 . Conceptual analyzer  305  recognizes the relationship between entities and helps in content categorization. Quantitative analyzer  306  is provided to analyze the data intensive subject matter from the content feed  301 . Data intensive information can include numerical data, comparative data, data patterns over a period of time and other similar quantity related information. 
         [0038]    Pattern analyzer  307  is another component of NLP which analyzes the patterns, trends, arrays, series and other similar aspects from the content feed  301 . Also, pattern analyzer  307 , along with semantic analyzer  302 , categorizes the content feed  301  and also able to apply the various statistical models to the data to make possible projections, provide probabilities, predictions as relevant. Theme analyzer  308  is another component of NLP that identifies the central theme of the subject matter from the content feed  301 . 
         [0039]    As illustrated in  FIG. 4 , according to an embodiment, the training module  401  is provided to capture the user&#39;s prior knowledge data  402 , training data  403 , and user preferences data  404 . User&#39;s knowledge data  402  that includes user&#39;s knowledge levels regarding the content requested by the user among other aspects associated with the user is captured by the training module  110  and transmitted to the preferences manager  408 . Training data  403 , related to user, such as user&#39;s education, working field, areas of interest, content reading history among many other similar aspects related to user is captured by the training module  110  and transmitted to the preferences manager  408 . User preferences data  404 , including user&#39;s preferences on the content to be delivered and the content presentation type and format is captured by the training module  110  and transmitted to the preferences manager  408  for further usage at steps of mapping, processing and transforming the content. 
         [0040]    Analytics module  405  is provided to monitor the user&#39;s activities and generate the user activity data  406  and to collect the user&#39;s feedback  407  during the presentation of personalized content according to the present invention. Analytics module  405  closely monitors user&#39;s behavior and actively tracks the user&#39;s activities during the personalized content presentation to generate the user activity data  406  thereby to understand user&#39;s behavior and preferences about the personalized content presented to the user. The user&#39;s activities monitored comprise at least one among the click events, download events, usability analysis, reading time and scroll data. Analytics module  405  further transmits captured user activity data  406  to the preferences manager  408  for further usage by the system. In addition to user activity data, analytics module  405  collects user&#39;s feedback  407  explicitly during content presentation by providing the user a set of queries, options to choose from on the content type an presentation type and format, and other feedback mechanisms. User&#39;s feedback  407  collected by the analytics module  405  is transmitted to the preferences manager  408  for further usage by the system. Both the implicit and explicit preferences help system to find the effectiveness on the user, rendered presentation format, rendered layout of the content being presented to user. 
         [0041]    Preferences manager  408  is provided for storing and managing the various preferences and activities of the user received from the training module  401  and the analytics module  405 . Preferences manager  408  consists of implicit preferences manager  409  for managing user&#39;s implicit or non-voluntary preferences. Implicit preferences manger  409  uses training module  401  and analytics module  405  to derive the implicit preferences of the user. User&#39;s implicit preferences include but not limited to user&#39;s geography, limited knowledge of user&#39;s location learned from the browser used by user, preferences about media type. Preferences manager  408  also consists of explicit preferences manager  410  for managing user&#39;s explicit or voluntary preferences about the content and presentation type and format. Explicit preferences include but not limited to preferences about the content type, media type, presentation type and format, language, user&#39;s interests, content rating. Explicit preferences manger  410  uses training module  401  and analytics module  405  to derive the implicit preferences of the user. User profile data  129  stored on LDAP server  128  containing the specific user profile attributes such as user name, phone number and other contact details is used by preferences manager for obtaining explicit preferences. 
         [0042]    Another component of preferences manager  408  user activity tracker  411  is provided to manage the user&#39;s activities data  406  obtained by the analytics module  405  during the presentation of personalized content to the user. When there is conflict between similar-category of preferences between implicit and explicit categories, precedence is always given to explicit preference. For instance if the implicit preference manager  409  finds the user language as “Spanish” based on browser locale, but if user has specified “English” as preferred language explicitly, preferences manager  408  chooses “English” for content rendition. Preferences manager  408  further consists of a preferences store  412 , a database that preserves user&#39;s implicit and explicit preferences and the tracked user&#39;s activities to further supplement the mapping, and transforming of content. 
         [0043]    Content processing module  413  personalizes the content  309  received from learning module  300  in the content mapping step  414  and content transformation step  415  based on the analyzed and categorized content  309  received from the preferences manager  408  and learning module  300 . Analyzed and categorized content  309  is subjected to the mapping based on the user&#39;s prior knowledge data  402 , training data  403 , obtained from training module  401  and the data available with preferences manager  408 . A rules engine provided with the content processing module  413  allows flexible configuration of rules to personalize the visuals based on user&#39;s preferences. Rules engine obtains the user&#39;s preferences data available with preferences manager  408 . The system also enables the user to configure the rules using rules engine. 
         [0044]    Further, the content obtained from content mapping step  414  is transformed in the step  415  to generate the personalized content. According to  FIG. 5  content transformation module  501  is provided to transform the as-is content to the user&#39;s preferred friendlier presentation format considering various factors. The module is a part of content processing component which gets it input from learning module. The structured information obtained from the learning module  300  is further refined and transformed into preferred presentation type and format. In one of the embodiments of the present invention, the core idea transformer  502  uses the structured information obtained from the theme analyzer  308  and uses the bulleted summary format to fill the information. 
         [0045]    Categorization verifier  503  re-verifies the content categorization done by learning module  300 . The information of categories is further used for rendering the analogies and compare &amp; contrast information. Concept miner  504  parses the entire content and identifies all the sub-concepts and other concepts in the content. Concept miner  504  also extracts the content that support and complement the core concept of the content being presented to the user. Sequence transformer  505  uses the input from chronological analyzer  304  and transforms into the InfoGraphics format. Compare &amp; contrast module  506  uses the structured data obtained from theme analyzer  308  and concept miner  504  to identify all core concepts and sub concepts of the content and extracts the content with similar or matching category from knowledge repository. Compare &amp; contrast module  506  further uses the information on category, core concept and sub-concepts of the content while rendering the analogies during the content presentation. The content obtained from the chronological analyzer  304 , cause and effect transformer  507  is transformed into the InfoGraphics format. 
         [0046]    As illustrated in  FIG. 6 , Content presentation module  601  uses visual renderer  602  for rendering the personalized content received from the content processing module  413 . Visual renderer  602  can present the structured data of personalized content into various presentation types. The present invention supports various visual renderers such as Data visual renderer  603 , InfoGraphics renderer  604 , News graph renderer  605 , Bulleted list renderer  606 , Text-to-speech renderer  607 , Slideshow renderer  608 , Graphs renderer  609 , Concept Map renderer  610 , Theme renderer  611  among many other renderers. 
         [0047]    Data visualization renderer  603  can be used to render the content into highly effective visuals and to facilitate the user to quickly interpret the data being presented. InfoGraphics Renderer  604  is used to create the InfoGraphics (Graphics and Text) to represent the main concept and supporting data of the content which works on “show, don&#39;t tell” concept. InfoGraphics Renderer  604  is also used to visualize data intensive content such as census data, voting data, distribution data and other relevant data types. News Graph renderer  605  is used to create a news graph from the content. News Graph renderer  605  is mainly used to visualize the enormous amount of news content to visualize the news content and to discover emerging trends. News Graph renderer  605  includes features such as extracting the news highlights based on popularity, grouping of news based on languages, geographies, topics. 
         [0048]    Bulleted list renderer  606  lists the key points of the content to represent the concept with a list of supporting arguments such as feature list of a product, problems and solutions and other similar concepts. Text-to-speech renderer  607  converts the text to speech and reads through the displayed bulleted list of the content. Listening of the key points of content presented is more helpful to users who prefer the audio version of the content. Slideshow renderer  608  renders the key points of content in a slide show format containing the key highlights with graphics. Graphs renderer  609  presents the content data in a graphical format. Especially, when comparing the data intensive content, data presented in a graphical format is more effective on the user. Data intensive content such as financial data, budgetary data, and research data can be more effectively represented using InfoGraphics renderer  604  in combination with Graph renderer  609  that further enables the user to discover any subtle patterns in such data intensive content and to visualize the percentage in opinion, predictions, and polls in quantitative data. Concept map renderer  610  is used for depicting the complex subject matter in the content with inter-related concepts in concept map presentation type. Concept map presentation types are usually used for depicting hierarchy of entities along with their relationships, representing complex ideas and concepts. Theme renderer  611  presents the core idea of the subject matter in the user&#39;s preferred location on the content presentation layout. Theme renderer  611  also is capable of rendering the summary or any noticed pattern along with core idea and presenting at the central location of presentation layout to draw the attention of user. 
         [0049]    Analogy renderer  612  uses the core idea of the subject matter and the category of the subject matter in the content to identify a similar concept or concept belonging to the same category from the knowledge repository. Based on the analysis of learning module  300  on the content requested by the user, analogy renderer  612  retrieves the matching concepts and categories. Analogies form the core of cognition wherein new concepts and knowledge is learnt based on the similarities to the already acquired core concepts. User is also optionally enabled to classify the new concepts according to the similarities to the existing concepts by prototypical concept. Relevant content renderer  613  aggregates the links to content sources, articles, graphics, videos and other assets relevant to the subject matter of the personalized content presented to the user from online sources. For this purpose relevant content renderer  613  uses the in-built search engine to display the relevant online content along with the personalized content presented to the user. 
         [0050]    Compare and contrast renderer  614  is an optional renderer provided by the present system. Compare and contrast renderer  614  uses the content categorized by the learning module  300  and finds the content with a similar concept in the knowledge repository. Compare and contrast renderer  614  compares the features of and identifies similarities and differences between the concept of content categorized by the learning module  300  and the concept of content found from the knowledge repository and presents to the user along with the personalized content being presented to the user. Further the user is enabled to compare the core concept of personalized content with any concept of content available with knowledge repository. Multi-device renderer  615  helps in rendering the presentation on variety of user devices. Multi-device renderer  615  also helps in adjusting the page layout, reducing the graphics, choosing device friendly presentation types to effectively represent the concept on a given device. 
         [0051]    The foregoing renderers can be used in the present invention on standalone basis or in combination of other renderers to achieve various presentation types and formats and their combinations. The content types foregoing renderers can be used on should not be limited only to specified types of content and the renderers can be used on any type of the content as they are capable of rendering. In addition to the renderers, the presentation module  601  also provides a language translator  616  to translate the personalized content into user&#39;s preferred language obtained from preferences manager  408 . The language translator  616  is also capable of rendering the content in a user preferred language on multiple user devices. User can specify the preferred language as a part of user&#39;s preferences. 
         [0052]    A visual template library  617  is included in the content presentation module  601  to store the collection of presentation objects which can be used by the present system. Further any new presentation object can also be added to the visual template library  617 . Visual template extension framework  618  is an extension framework used to add more presentation objects to the content presentation module  601 . Visual Template library  617  is the library of all presentation types supported by the content presentation module  601 . Concept presentation module  601  populates the data from the analyzed and categorized content and populates the visual templates from the visual template library  617 . 
         [0053]    According to one embodiment of the present invention, the concept presentation module  601  determines that if the user has specified any preferred presentation type in the preferences manger  408 . On finding the user&#39;s preferences about the presentation type and format with preferences manager  408 , the content presentation module  601  further determines that if the personalized content can be appropriately represented in user&#39;s preferred presentation type and format for further rendering. The personalized content is subsequently filled into the appropriate visual template to create a final presentation of personalized content to the user. In event of unavailability of the user&#39;s preferences about the presentation type and format at the preferences manager  408 , the most appropriate presentation type suitable for the content is chosen by the content presentation module  601  for further rendering. 
         [0054]    Analytics module  701  as illustrated in  FIG. 7  is provided to monitor the user&#39;s activities and generate the user activity data  702  and to collect the user&#39;s feedback  703  during the presentation of personalized content according to the present invention. User activities can be tracked both implicitly and explicitly. Analytics module  701  closely monitors user&#39;s behavior and actively tracks the user&#39;s activities during the personalized content presentation to generate the user activity data  702  thereby to understand user&#39;s behavior and preferences about the personalized content presented to the user. The user&#39;s activities monitored comprise at least one among the click events, download events, usability analysis, reading time and scroll data. In addition to user activity data, analytics module  701  collects user&#39;s feedback  703  during content presentation by providing the user a set of queries, options to choose from on the content type an presentation type and format, and other feedback mechanisms. Analytics module  701  also possesses a user segmentation analyzer  704  for identifying the relationship among user profile attributes such as user&#39;s role, geography, language or any similar attribute and the actions such as usage of media type, preferred presentation type and format, online advertisement conversion rate or any other similar action. The relationship information is further consolidated to establish the apparent relationship between user attributes and the actions. The relationship established can be used by the content personalization module in customizing the user experience. 
         [0055]    Usability analyzer  705  tracks the effectiveness of presentation type and format by analyzing the metrics such as exit rate, video abandonment rate, user time spent on the web page. Layout analyzer  706  analyzes the effectiveness and usability of the presentation layout by measuring the events such as click events at core idea or theme presentation section on the page, click events on the right hand side section of the web page to determine the rate of change of contents such as conversion rate for advertisements, relevancy of aggregated related links, click events in the analogy section to determine the relevancy and effectiveness of analogies. Clickmap and download analyzer  707  measures the click events and download events to identify user&#39;s liking for a download type. Analysis of usability analyzer  705 , layout analyzer  706  and map and download analyzer  707  is used in improving the presentation type and format presented to the user. Also the analysis helps in measuring effectiveness and usability of analogies presented to the user and related information. 
         [0056]    An example web page presentation of personalized content according to the present invention is now described with reference to  FIG. 8 . An original content page,  801  is represented in a static web page layout which is pre-dominantly text based and may consist of one or more images. A presented content page,  804  is represented in accordance with the present invention which is presented with a personalized presentation type and format  807  in accordance with the user preferences, where they exist with system or a presentation type and format which suit the subject matter in the personalized content. The presented content page,  804  further comprises an image  805  representing the content being presented, a user&#39;s preferred hotspot location  806  where the central idea, summary and patterns noticed are combined and presented to the user for to drawing user&#39;s attention and for quick understanding of the details of personalized content. 
         [0057]    Further, user is enabled to change the presentation type using the presentation type selector  808  to get the varied views of the same content and can also rate the content using review and rating widget  809 . The rating is further used by the system to further improve the personalization of content; this is done by updating the personalized content mapping information. The related content  810  such as links to content sources, articles, graphics, videos and other assets, relevant to the subject matter of the personalized content, aggregated by the relevant content renderer  613  from online sources are presented to the user on the right hand corner of the presented content page  804 . Related content  810  is sourced from online using in-built search engine. This would help user to explore the topic further. 
         [0058]    The system also determines with the knowledge repository the content with any other topics related to the same category of personalized content to render at  811  in right hand side section of the page. It helps user to build upon the existing knowledge and relate the current subject matter by relating its core concepts to the knowledge user already possess. Analogy renderer presents category and analogies of the content  812  which still enhance user&#39;s understandability. Analogies are drawn from user&#39;s acquired knowledge or more-easy-to-understand real-world samples. User can make sense of the topic by “similarity” effect. The system also presents the content comparison and contrast  813  where user can compare the core concept with any available concept in the knowledge repository. Content comparison and contrast  813  is an optional content which displays if system finds any similar topic for comparison in the knowledge repository. This again helps in re-enforcing the subject matter being presented to the user. The overall objective of the presented content page  804  is to highlight the key points, summary, category of the content, present the concept in intuitive presentation type and simplify the complex content. 
         [0059]    The foregoing description is presented to enable a person of ordinary skill in the art to make and use the invention and is provided in the context of the requirement for a obtaining a patent. The present description is the best presently-contemplated method for carrying out the present invention. Various modifications to the preferred embodiment will be readily apparent to those skilled in the art and the generic principles of the present invention may be applied to other embodiments, and some features of the present invention may be used without the corresponding use of other features. Accordingly, the present invention is not intended to be limited to the embodiment shown but is to be accorded the widest scope consistent with the principles and features described herein.