FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
1. Title of the invention:
PERSONALIZED CONTENT GENERATION
2. Applicant (s)
NAME NATIONALITY ADDRESS
TATA CONSULTANCY Nirmal Building, 9th Floor, Nariman Point,
Indian
SERVICES LIMITED Mumbai- 400021, Maharashtra, India
3. Preamble to the description

COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
The present subject matter relates, in general, to content generation and, in
particular, to personalized content generation.
BACKGROUND
Developments in the field of information technology have resulted in
production of fast, secure and reliable computing and communication systems. The computer-based systems have been used as content generation systems to generate content which may be used for imparting education to an end user. Alternatively the content generation systems may also be used to develop assessment tests to measure the knowledge level, expertise, skill set of an end user. The content generation systems have many advantages over the conventional pen and paper models, such as: convenient scheduling of learning or assessment sessions; standardized questions and content; uniform evaluation of responses to questions; quick and fast processing resulting in the end user being able to view his score in an assessment test almost immediately, etc.
However, the content generated by the conventional content generation
systems (such as content for imparting knowledge, questions for assessment tests) may be based on the perception, background and experience of the content developer or the individual creating the assessment material. The content and the assessment material usually relate to only a particular environment that the test setter or content developer has chosen to create. It could be the environment with which the test setter or the content developer is familiar, such as his demographic information, educational qualification, work profile, etc. In such a case, the content may not be suitable to a wider spectrum of users from a different background. The users may not be able to relate to the content generated by the conventional content generation systems as a result of which their learning may be hampered. Hence, the conventional content generation systems fail to properly assess one or more candidates based on their knowledge level, skill sets and abilities.
SUMMARY
The subject matter described herein relates to system and methods of
personalized content generation for education and evaluation based on the user profile

information, which is further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
In accordance with one embodiment of the subject matter, the personalized
content generation includes determining customizable portions in a content, classifying the customizable portions for example as a profile based placeholder or a content based placeholder and substituting the customizable portion by a value based on the classification and pre-defined rules.
BRIEF DESCRIPTION OF DRAWINGS
The above and other features, aspects, and advantages of the subject matter
will be better understood with regard to the following description, appended claims, and accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference number in different figures indicates similar or identical items.
Fig. 1 illustrates a network environment implementing a personalized content
generation system, in accordance with an embodiment of the present subject matter.
Fig. 2 illustrates a personalized content generation system, in accordance with
one embodiment of the present subject matter.
Fig. 3 illustrates an exemplary method for personalized content generation
based on user profile information, according to an embodiment of the present subject matter.
DETAILED DESCRIPTION
The present subject matter relates to systems and methods for personalized
content generation. In one embodiment, content generated are based on an end user's profile information.
The adoptions of conventional content generation systems have been very slow
especially in developing countries or rural areas. Such conventional systems generate content for dissemination of knowledge based on a compendium of usable content portions. The usable content portions can be fetched by such systems for the generation of content. Examples of such content include, but are not limited to, questionnaire, tutorials, memos, and any content that can be utilized for dissemination of knowledge.

Conventional content generation systems usually generate content based on
portions, which in general, are heavily biased in favor of the perception and background of the content developer and/or the system administrator. For example, in the education industry, the conventional content generation system renders learning material that may have been created by qualified academics placed abroad. Thus, the end users sometimes fail to relate to the context of the contents rendered by the content generation system. In such a case, the content thus generated may not readily appeal to the user having a different background. As a result, the content used by the end users, i.e., the students, may seem to be alien as they may not be able to fully relate to the example provided in the generated content. This may arise due to differences in the perception, background, education, cultural background, demographics, etc., of the end users and the content developer. Furthermore, within countries having a diverse cultural background, such as India, content which is more familiar within a certain cultural community may be a novelty for another community. This may lead to restricted knowledge dissemination amongst the users.
Systems and methods for personalized content generation based on user profile
information are described. In one embodiment, the personalized content generation system obtains profile information of the end user. As will be appreciated by a person skilled in the art the profile information of the end users can be gathered from various sources, such as from an external storage storing data entered by the end user, information available from other sources such as social networking portals, community portals, records maintained by various government and private agencies, etc.
For generation of personalized content, one or more content templates are
obtained. The content templates further include one or more customizable portions. Each of the customizable portions is identified through placeholders. For example, the customizable portions may be defined in a pre-defined format such as starting with '@' sign, etc. It would be appreciated that other ways for identifying such customizable portions would also be within the scope of the present subject matter. The customizable portions are further associated with meta-information.
In one implementation, the customizable portion can either be a profile-based
placeholder or a context-based placeholder. The customizable portions can be identified as

either of the placeholders based on meta-information. The profile-based placeholder is replaced by a value determined based on the profile information of the user. On the other hand, the values of the context-based placeholder can be determined based on one or more predefined rules. The predefined rules ensure that the personalized content has the same standard in terms of difficulty level, ease of comprehension.
Moreover, in another implementation, the personalized content generation
system may collect statistical data regarding the response to the personalized content
generated. For example, the personalized content generation system may determine whether a
certain question is too easy or too difficult for certain sections of the population based on their
background, educational qualification, demographics, skill set, etc. The determination may be
directly based on end user feedback or may be inferred from the responses given by the user
to a particular question. Additionally survey results conducted by third parties may also be
incorporated into the rules repository. For example, there is a notion that Indians relate more
to the sports of cricket, whereas Brazilians relate more to the sports of football. The same may
be taken as one of the factors during the process of personalized content generation.
Thus the personalized content generation system personalizes the content
based on the profile information of the user without changing the objectives of the content.
This leads to better user association with the content, as compared to the conventional content
generation systems, because the end users can relate to the personalized content. As will be
appreciated, this would lead to better understanding of the context of the content and result in
efficient education and accurate assessment of a end user's knowledge, skill set and
capabilities. These and other features and advantages of the personalized content generation
system are described in further detail in conjunction with the following figures.
The following disclosure describes systems and methods for personalized
content generation based on user profile information. While aspects of the described systems
and methods can be implemented in any number of different computing systems,
environments, and/or configurations, embodiments for the web page classification system are
described in the context of the following exemplary system(s) and method(s),
ft will be appreciated by those skilled in the art that the words during, while,
and when as used herein are not exact terms that mean an action takes place instantly upon

initiating action; but that there may be some small but reasonable delay, such as a propagation delay, between the initial action and the reaction that is initiated by the initial action. Additionally, the word "connected" is used throughout for clarity of the description and can include either a direct connection or an indirect connection.
Fig. 1 illustrates a network environment 100 implementing a personalized
content generation system 102 for generating personalized content based on a user's profile, according to an embodiment of the present subject matter. In the network environment 100, the personalized content generation system 102 is connected to a network 104. One or more client devices 108-1, 108-2, .. 108-N, collectively referred to as client devices 108, are also connected to the network 104.
The personalized content generation system 102 can be implemented as any
computing device connected to the network 104. For instance, the personalized content generation system 102 may be implemented as mainframe computers, workstations, personal computers, desktop computers, multiprocessor systems, laptops, network computers, minicomputers, servers and the like. In addition, the personalized content generation system 102 may include multiple servers to perform mirrored tasks for users, thereby relieving congestion or minimizing traffic.
The personalized content generation system 102 is connected to the client
devices 108 through the network 104. Communication links between the client devices 108
and the personalized content generation system 102 are enabled through a desired form of
connections, for example, via dial-up modem connections, cable links, digital subscriber lines
(DSL), wireless or satellite links, or any other suitable form of communication.
The network 104 may be a wireless network, a wired network, or a
combination thereof. The network 104 can also be an individual network or a collection of many such individual networks interconnected with each other and functioning as a single large network, e.g., the internet or an intranet. The network 104 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet and such. The network 104 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control

Protocol/Internet Protocol (TCP/IP), etc., to communicate with each other. Further, the network 104 may include network devices, such as network switches, hubs, routers, host bus adapters (HBAs), for providing a link between the personalized content generation system 102 and the client devices 108. The network devices within the network 104 may interact with the personalized content generation system 102 and the client devices 108 through communication links.
In one implementation, the personalized content generation system 102
includes a placeholder identifier 108. In one implementation, to generate content for a user (say user of one or more of the client devices 104) placeholder identifier 108 fetches a plurality of content templates. The content templates include portions of static and customizable content. The customizable content can be identified through one or more placeholders within the content template. In one implementation, the placeholder identifier 108 identifies the customizable portions within the content template based on semantic meta information. Once the content has to be generated, the relevant placeholders can be substituted based on profile information of the user requesting the content that is to be generated. The profile information can be gathered from a profile repository, or through various social networking sites to which the user may be subscribed to. t. In one embodiment., the values used to substitute the placeholders are stored in a repository for future use. Further pre-defined rules ensure that the inter relation between placeholders is maintained thus preserving the sanity of the content.
The placeholder indicating the customizable portions can be further of two
types - profile based placeholders and context-based placeholder. As also indicated previously, the type of the placeholder can be determined by the content creator based on semantic meta-information that is associated with the relevant placeholder during the creation or reuse of a content. The profile-based placeholder is for being customized with data that is generated based on the profile information of the user. For example, the content may be such that it describes an individual. The name of the individual would be a profile-based placeholder. In such case, the name profile-based placeholder can be customized with a name is that is generated based, say on the geographic location, nationality, etc., of the user. In the present example, the name profile-based placeholder can be customized with a name like

"Bharat', "John", for users that are based in India, US, respectively. It would be appreciated
that the profile-based placeholder may include other factors, such as names of places,
profession of people depicted in the content, means of transports, entertainment means, etc.
Similarly, the context-based placeholders are for being customized with
information that is contextually related to the value of the profile-based placeholders, and/or
other placeholders. For example, when the mode of transport, due to personalization of the
profile-based placeholder, is changed from an air plane to say a car, the names of the places,
the distance between the places, the distance between the places, the average speed of the
mode of the transport, etc., need to be updated accordingly. In such a case, parameters such
lower speed would be associated with profile-based placeholder being a car, as compared to
very high value of speeds associated with the profile-based placeholder being an airplane.
Returning to the operation of the personalized content generation module 102,
the placeholder identifier 108 identifies the profile-based placeholder and the context-based placeholders within the content templates. Once the profile-based placeholder and the context-based placeholders are identified, the placeholder identifier 108 substitutes or customizes the profile-based placeholder. For customization of the profile-based placeholder, the placeholder identifier 108 generates one or more data based on the profile information of the end user. Once such data is obtained, the placeholder identifier 108 substitutes the profile-based placeholder with obtained data.
Corresponding to the data substituted in place of the profile-based placeholder,
the placeholder identifier 108 further determines the relevant contextual information. In one implementation, the contextual information is determined based on one or more association rules for the data substituted. Depending on the data used for substituting in place of the profile-based placeholder the relevant contextual information is obtained and substituted in place of the context-based placeholders.
Once both the profile-based placeholder and the context-based placeholders
have been customized, the final personalized content (which includes the customized profile-based placeholder, customized context-based placeholders, and the static content) is generated for the end user through one or more client devices 108. As will be appreciated, the content thus generated would be personalized based on the user profile information, making it easier

for the user to associate with the context of the content. This would lead to keener interest in the subject matter, and better and deeper understanding of underlying concepts. These and other aspects are described further in conjunction with Fig. 2.
Fig. 2 illustrates the personalized content generation system 102 in accordance
with one embodiment of the present subject matter. The personalized content generation system 102 includes processor(s) 202, a memory 204 coupled to the processor(s) 202 and I/O interface(s), referred to as interfaces 206 to facilitate communication with other devices and systems.
The processor(s) 202 can be a single processing unit or a combination of
multiple processing units. The processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) 202 are configured to fetch and execute computer-readable instructions and data stored in the memory 204.
The interface(s) 206 may include a variety of software and hardware
interfaces, for example, interface for peripheral device(s) such as a keyboard, a mouse, an external memory, a printer, etc. Further, the interfaces 206 may enable the personalized content generation system 102 to communicate with other computing devices, such as web servers and external databases. The interfaces 206 may facilitate multiple communications within a wide variety of protocols and networks, such as the network 104, including wired networks, e.g., LAN, cable, etc., and wireless networks, e.g., WLAN, cellular, satellite, etc. The interfaces 206 may include one or more ports for connecting the personalized content generation system 102 to the network 104 and or client devices 108.
The memory 204 can include any computer-readable medium known in the art
including, for example, volatile memory such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 204 includes modules 208 and data 210.

The modules 208 include the placeholder identifier 108, a content generation
module 212 and other module(s) 214. The other module(s) 214, in general, include routines,
programs, objects, components, data structures, etc., that perform particular task or implement
particular abstract data types and may include programs that supplement applications
implemented by the personalized content generation system 102. The data 210 includes a
content repository 216, which stores various types of content (such as text, images, media
files, animations, etc.) may be used for personalized content generation. The content
repository 216 also stores various content templates like question paper templates, etc.. which
may be used to generate personalized content for the end user. Once personalized content is
generated, say by the content generation module 212, the same can be stored in the content
repository 216 by the system administrator or the content developer. Further the personalized
content generation system 102 can be configured to automatically add new and related content
from various sources such as web portals using conventional techniques such as web crawlers.
The data 210 further includes a profile repository 220 which stores the profile
information of the end user from various sources such as data entered by the end user in the personalized content generation system 102, information available from other sources such as social networking portals, community portals, records maintained by various government and private agencies, etc.
The data 210 also includes a rules repository 218 wherein various parameters
required for personalized content generation and their inter-relation is stored. Further the rules
repository 218 may also store conventionally known knowledge like mathematical theorems,
universally accepted laws, etc. For example, one rule may be stated as force is equal to mass
times acceleration. Other rules may be stated so as to maintain a practical context with the
content. For example, if the subject under consideration involves a train, then such rules
would specify that the speed of the train would not exceed 200 kmph. Similarly, if the subject
under consideration involves a bicycle rider, a speed not exceeding 30 kmph would be
considered practical, which can be specified by the rules within the rules repository 214.
The data 210 may also include other data 220 which may be used for other
functionalities of the personalized content generation system 102. For example, in one implementation, the other data 220 may include user feedback and response to the generated

personalized content, which may be used to enhance the quality of future personalized content.
In operation, a user of one or more of the client devices 108 may start
interacting with the personalized content generation system 102. Subsequently, the placeholder identifier 108 may retrieve the content template, say from content repository 216. A content template can be considered as a framework on which the content that is to be customized is based upon. Generally the content template includes one or more placeholders that may be substituted with suitable values. As also indicated previously, the values that are utilized for customization can be retrieved from the content repository 216 or the profile repository 220.
Once the content templates are received, the placeholder identifier 108
identifies the customizable portions based on the profile-based placeholders and the content based placeholders. In one implementation, the placeholder identifier 108 identifies the profile-based placeholders and the context-based placeholders based on semantic meta information of the retrieved content. The semantic meta information, in one implementation, is stored in other data 220.
Once the profile-based placeholders and the context-based placeholders are
identified, the placeholder identifier 108 obtains profile-based information associated with the end user from the profile repository 220. As will be appreciated by a person skilled in the art, the profile-based information can be based on previously provided personal or professional data by the end user, or can be gathered from other external sources, such as through one or more online social networking forums.
Based on the profile information gathered by the placeholder identifier 108. the
content generation module 212 obtains the relevant values for customizing the profile-based placeholders and the context-based placeholders from the placeholder values 222. As discussed previously, the content generation module 212 customizes the profile-based placeholders by substituting with values that are obtained based on the profile information included within the profile repository 220, For example, for the nationality of end user nationality, the name, places, names of sports, would be such that are more likely to be recognizable by an individuals of that nationality. For example, for an Indian national, the

likely values that will be selected for names, places, and names of sport would be "Bharat", "Chennai", and "Cricket", respectively. Similarly, for a US national, a likely value of name of sport would perhaps be baseball, basketball, etc. It would also be appreciated that distinctions between the profile-based placeholders need not be based on the nationality only. In one implementation, the distinctions can be based on the geographic location or the hometown of the user, which may well lie within the boundaries of one country as well. In such a case, the profile-based placeholders would be substituted by values that are based on the hometown. As will be appreciated by a person skilled in the art, any levels of granularity can be provided for the profile-based placeholders.
Additionally, in one embodiment, the content generation module 212 retrieves
all the profile information of the end user which is stored in the profile repository 220. The content generation module 212 further analyzes all the profile information available of an end user and generates a consolidated profile. Further the content generation module 212 may be configured to update the profile information stored in the profile repository 220 at regular intervals or on user input. The consolidated and updated profile of the end user is stored in the profile repository 220
Once the relevant values for customizing the profile-based placeholders are
obtained, the content generation module 212 further obtains values that can be used for customizing the context-based placeholders. As also described previously, values for the context-based placeholders are such that they are contextual to the values obtained for the profile-based placeholders. This would be better understood with the following example, provided for purposes of illustration only. The example as provided should not be construed to be a limitation.
For example, content retrieved from the content repository 216 may be in form
of a question: 'The <> in its journey from <> covers a distance of <> kilometers in <>. Determine the average speed.' Herein the underlying concept, which the question attempts to test, is the capability of the end user to apply the relevant equation that are necessary for the calculation of the average speed. The placeholder identifier 108 on receiving the content template determines <>, <>, <>

the profile-based placeholders, while the placeholder <>, <> would be determined as context-based placeholders. In one implementation, the context-based placeholders may also include additional description that may be descriptive of the profile-based placeholder. For purposes of illustration only, the «Transportation Mode Name» may be associated with a descriptive text that may also be used as part of the context-based placeholder «Transportation Mode Description». The context-based placeholder «Transportation Mode Description» can then be customized with the related description. For example, the personalized content generated can include related description when the placeholder «Transportation Mode Name» is a train, submarine, aircraft, and so on. As will also be appreciated by a person skilled in the art, the subject matter provides for a flexible mechanism, which would result in a highly customizable personalized content generation. The content generation module 212 then proceeds to customize the content based on the profile of the end user who wishes to answer the question. For example, the placeholder identifier 108 may determine that the end user is based in Kolkata but is studying in New Delhi. In such a case, the content generation module 212 may correlate such profile information to obtain values for the placeholders «Starting Location», «Destination» as Kolkata and New Delhi. Similarly, for an end user based in Cochin, the content generation module 212 obtains the values as Chennai, Vishakhapatnam for the «Starting Location», «Destination» placeholders. Further the content based placeholders are replaced based on the new context of the question. For example, if the «Transportation Mode Name» is Rajdhani Express, the time taken can be provided accordingly for the distance between the two cities. In one implementation, the values can obtained based on one or more rules provided in the rule repository 218. Accordingly, when the end user has these values, he/she will be in a position to answer the question proposed.
It may also be the case that based on the profile of the end user, the «Starti'ng
Location», «Destination» placeholders be replaced by two landmarks within a city, then the placeholder «Transportation Mode Name» will change from "Rajdhani Express" to say a metro train or a bus or a car or even a cycle as mentioned in the rules repository. Further the distance between the places and the time taken must also be changed accordingly so that the question does not become absurd. For example, statistics reveal the average speed of a cycle

is 15-18 kilometers per hour (kmph) whereas that of an express like "Rajdhani Express" is 80-140 kmph. Thus the rules defined in the rules repository 214 ensures that scenarios where the answer obtained for the speed of a cycle is 100 kmph, or for the speed of a superfast train is 8 kmph are ruled out. Further standards defined in the rules repository 214 ensure that the level of the difficulty of the question does not change during the personalization. In this scenario rules retrieved from the rules repository 214 ensure that the calculation required to solve the question stated in the example does not change from 1447 kilometers divided by 16 hours 55 minutes to as easy as say 40 kilometers divided by 2 hours.
Moreover, rules retrieved from the rules repository 214 ensures that the
quality, standard and the sanity of the personalized content is not affected. Further the content
generation module 212 runs one or more self checks retrieved from the rules repository 214 so
as to eliminate errors which might have crept in during personalizing the content such as
spelling mistakes, text trims, tense mismatch, etc. Thus the content generation module 212
ensures that the personalized content so generated in error-free and enhances user experience.
In one implementation, the rule repository 218 may further include abstraction rules to ensure
that the level of difficulty and the ease of comprehension of the content are maintained. The
abstraction rules prevent the personalized content from becoming absurd or impractical.
Further in one embodiment, the personalized content generation system 102
may take user feedback of the personalized content either directly or infer the same from responses given during an assessment test and may generate various reports based on it. The reports help in the decision making process of the system administrators and the content developers so as to enhance the quality of the personalized content. Additionally the personalized content generation system 102 may generate reports that indicate the standards of the personalized content. For example, certain sections of the population, say engineers, found certain type of content, say quantitative questions, too easy. Based on the reports, the standard of future personalized content may be enhanced so as the objectives of generation of personalized content, be it imparting education, assessment of the end user, etc., may be met in an optimized manner.
It would also be appreciated that the example as provided exemplifies
scenarios where the personalized content is in the form of questions. However, other forms of

content, such as tutorials, manuals, etc., would also be included within the scope of the present subject matter.
Fig. 3 illustrates an exemplary method 300 for personalized content generation
based on user profile information, according to an embodiment of the present subject matter. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
The order in which the method 300 is described is not intended to be construed
as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300, or an alternative method. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. The method 300 is presently provided for personalized content generation. Jt will be apparent that the method 300 may be implemented for personalized content generation based on other parameters with modifications as known by those skilled in the art.
At block 302, the content template to be personalized is obtained. For example,
the content template can be received from the content repository 216. The content template may be in form of text, images, audio files, video files, multimedia files, etc. Further the content template may also be entered in real time by the content developer or the test setter or the system administrator.
As shown in block 304, the user profile information is retrieved. For example,
the placeholder identifier 108 retrieves profile-based information from the profile repository 220. in one implementation, the profile repository 220 includes personal information, demographic information, education, skill set, work experience, interests, hobbies, etc.

Further the user profile information may be supplemented with information of the user
available from third party sources such as social networking sites, community sites,
government records, etc. In one embodiment, the user profile may be in form of a
consolidated report generated based on all the information available related to the user,
At block 306 the content template retrieved for personalization is processed
and customizable portions in the content template are identified. For example, the placeholder identifier 108 identifies the customizable templates based on profile-based placeholders and the context-based placeholders includes within the content template. In one implementation, the placeholder identifier 108 identifies the profile-based placeholders and the context-based placeholders based on abstraction rules and the semantic meta-information. The profile-based placeholders are replaced by values retrieved from the user profile information. In one implementation, the substitution may not be a direct replacement of the placeholder with a value and may involve pre-processing of the value such as case correction, text trimming, tense matching, etc. The context-based placeholders are replaced based on the context of the content.
At block 308, the values for customizing the profile-based placeholders and the
context-based placeholders are identified. In one implementation, the content generation module 212 determines the values for customizing the profile-based placeholders based on the information associated with the user stored in the profile repository 220. Furthermore, the content generation module 212 determines the context-based placeholders values that are contextually related to the values used for customizing the profile-based placeholders. In one implementation, the values for the profile-based placeholders and the context-based placeholders are stored in the placeholder values 222. In another implementation the values may undergo processing such as tense match, text trimming, case correction, grammatical corrections, etc., before being substituted in the placeholder.
At block 310, the personalized content is rendered based on the content
template, content of the content and the user profile information. For example, the content generation module 212 generates the personalized content based on the customized profile-based placeholders, the customized context-based placeholders and the static information. As will be appreciated by the person skilled in the art. the personalized content thus generated

would be familiar to the user to a greater extent thereby increasing the chance of the end user readily accepting and solving a question provided. Thus the true potential and merit of the candidate can be determined. In case of generating a learning material or study material, the personalization of the content makes it easier for the end user to associate with or connect to the learning material or study material leading to better understanding of the underlying concepts portrayed in the learning material or study material.
Thus the personalized content generation system 102 generates personalized
content based on the user profile information. Although embodiments for the personalized content generation system 102 for generating personalized content based on user profile information have been described in language specific to structural features and/or methods, it is to be understood that the invention is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as exemplary embodiments for the personalized content generation system 102 and methods.

We Claim:
1. A computer implementable method for personalized content generation, the method
comprising:
determining at least one customizable content based on at least one of profile-based placeholder and context-based placeholder within a content template;
obtaining values for customizing the at least one of the profile-based placeholder and the context-based placeholder, wherein the values for customizing the profile-based placeholder are obtained based on profile information associated with a user; and
generating personalized content based in part on customized profile-based placeholder and the context-based placeholder.
2. The method as claimed in claim 1 wherein the determining is based on the meta semantic information associated with the content template.
3. The method as claimed in claim 1, wherein the content template includes at least one of a text file, an image file, an audio file, a video file, and a multimedia file.
4. The method as claimed in claim 3, wherein the profile information includes at least one of the demographic information, educational background, work experience, skill set, . interests and web footprint of the user.
5. The method as claimed in claim 1, wherein the values for customizing the context-based placeholder are contextually related to values obtained for customizing the profile-based placeholder.
6. The method as claimed in claim 5, the method further comprising customizing the profile-based placeholder and the context-based placeholder by:
substituting the profile-based placeholder with the values for customizing the profile-based placeholder; and
substituting the context-based placeholder with the values for customizing the context-based placeholder.

7. The method as claimed in claim 1, wherein the personalized content is standardized
and calibrated based on pre-defined rules.
8. A personalized content generation system (102) comprising:
a processor (202); and
a memory (204) coupled to the processor (202), wherein the memory (204) comprises:
a placeholder identifier (108) configured to at least one customizable content based on at least one of profile-based placeholder and context-based placeholder within a content template; and
content generation module (212) configured to generate personalized content based on customizable content.
9. The system (102) as claimed in claim 8, wherein the content generation module (212) generates personalized content based on at least one of content template, user profile information and predefined rules.
10. The system (102) as claimed in claim 9, wherein the content generation module (212) is configured to customize the profile-based placeholder based at least on the user profile information.
11. The system (102) as claimed in claim 9, wherein the content generation module (212) is configured to customize the context-based placeholder based on values used for customizing the profile-based placeholder and the predefined rules.
12. The system (102) as claimed in claim 8, wherein the personalized content includes one of assessment tests, questionnaires, manuals, and tutorials.
13. The system (102) as claimed in claim 8, wherein the system (102) further comprises a profile repository (220) to store user profile information retrieved from one of a social networking sites, community portals and third party records.

14. A computer-readable medium having embodied thereon a computer program for executing a method for personalized content generation, the method comprising:
determining at least one customizable content based on at least one of profile-based placeholder and context-based placeholder within a content template;
obtaining values for customizing the at least one of the profile-based placeholder and the context-based placeholder, wherein the values for customizing the profile-based placeholder are obtained based on profile information associated with a user; and
generating personalized content based in part on customized profile-based placeholder and the context-based placeholder.