DESC:FIELD OF THE INVENTION
[001] The present invention relates to a system and method for auto assigning archetypes to consumers, more particularly the invention relates to a platform that enables auto-identifying and assigning the consumers into a plurality of archetypes in real time based on digital content engagement and consumer psychographics.
BACKGROUND OF THE INVENTION
[002] In today’s world, all the digital consumers are exposed to an endless amount of content that is available on the various social media platforms, online shopping platforms and the like. These consumers preferably do not consume the content in a homogenous or uniform way. Each one of the consumers consume the content differently as each consumer has a unique or different personality. Archetypes are driven by personality in content consumption which further influences the behaviour of the consumers. Based on the behavioural data of the consumers, psychometric profiles are created. In the conventional arts, the information regarding the psychometric profiles cannot be used for advertising. Archetype is a psychographic construct representing the latent and innate collection of habitual behavioural and emotional patterns and cognitions that evolve from social, biological, and environmental influences that define a consumer. It also represents and codifies the consumption and reaction patterns of the consumer to different stimuli across the digital and offline platforms. There are three different approaches of personality discovery, viz., Automatic Personality Recognition (APR), Automatic Personality Perception (APP) and Automatic Personality Synthesis (APS).
[003] APR aims at discovering and unearthing traits through self-assessment, which is psychometric – psychographic. Further, the APP relies on inferring the personality and behavioural patterns through proximal cues., i.e., through the judgment based on others perception or through continuous monitoring of the individual in different situations - offline or online. Furthermore, APS depends on the data from distal cues aimed at extracting the behavioural traits. APR functions as a base undertone by generating intelligence in a synthetic sample of the people and their archetype configurations, extrapolating to larger masses. APP and APS are data-driven exercises that consume data in various forms to infer behavioural traits to discover patterns and finally arrive at archetypes iteratively and comprehensively, analysing all the data signals. The reaction to stimuli plays a large part in honing or shaping the skews influencing the archetype determination. When it comes to social interactions, an individual displays diverse personae, and the collective and iterative modelling of such diverse personae depend on contextual and scenario data and reconstruction.
[004] Consumers today have access to a plethora of information that influences their perspectives, opinions and interests. Consumers are much more than their gender, age, societal or marital status, or sexual orientation. This evolution of the consumer has profound implications for how they consume content, especially on the web. Demographics alone, give marketers a very hazy outline of their audience. They understand their audience’s challenges, but not where to find them and what really moves them to action. Demographic profiles fails to explain why people buy, why they make the choices they do, and what appeals to them on a personal level. For example, every time a marketer sends two 34-year-old mothers the same advertisement, the odds are that the ad is being wasted on one of them. The reason is that they both may be of the same age, but their interests and values will likely be different. If they are targeted on the basis of their demographic profiles, the content has a less chance of resonating with them and engaging them. Marketers, therefore, need to look beyond demographics, and use richer insights to understand the attitudes and motivations of their target audience.
[005] Hence, there is a need for a method that can activate the consumer profiles or the psychometric profiles online by identifying the psychometric profiles and then assigning the archetypes.

OBJECTS OF THE INVENTION
[006] Some of the objects of the present invention aimed to ameliorate one or more problems of the prior art or to at least provide a useful alternative are listed herein below.
[007] An object of the present invention is to enable a brand to bid on the psychometric profiles of the consumers in real time and by behavioural segmentation data of the consumers as well.
[008] Another object of the present invention is to make the communication between a brand and a publisher efficient.
[009] Another object of the present invention is to provide a platform wherein the publisher and the brand can trade on their first party data sets when another entity kills the third party cookies.
[0010] Another object of the present invention is to provide a massive powerful research platform for media measurement tool and cross media measurement.
[0011] Another object of the present invention is to provide a method that deploys the psychometric profiles in real time and then gives the output or media measurements in real time by archetypes wherein the psychometric and demographic data both are obtained.
[0012] Other objects and advantages of the present invention will be more apparent from the following description when read in conjunction with the accompanying figures, which are not intended to limit the scope of the present invention.
SUMMARY
[0013] This summary is provided to introduce concepts of the invention related to a system and method for auto assigning archetypes to consumers, as disclosed herein. This summary is neither intended to identify essential features of the invention as per the present invention nor is it intended for use in determining or limiting the scope of the invention as per the present invention.
[0014] In accordance with an embodiment of the present invention, there is provided a method for for assigning archetypes to a plurality of users of a website. The method comprises: administering, by a server, an archetype indicator test to a first set of users of the website; acquiring, by the server, a first set of responses to the archetype indicator test from the first set of users; collating, by the server, the first set of responses to determine a user classification proportion per archetype; determining, by the server, a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website; obtaining, by the server, a digital footprint data of the plurality of users from a user profile database; constructing, by the server, a contextual scenario based on a sequence of activities of the user on the website and the digital footprint data of the user; mapping, by the server, the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme; creating, by the server, an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors; and assigning, by the server, an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.
[0015] In an aspect, the archetype indicator test is executed periodically by the server on the user profile database.
[0016] In an embodiment, determining, by the server, a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.
[0017] In an aspect, the method uniquely identifies a user.
[0018] In an embodiment, constructing the contextual scenario includes: detecting a first pattern within a session of the user on the website, and detecting a second pattern across multiple sessions of the user on the website; and wherein the server determines the theme based on first and second patterns of the user across the multiple sessions of the user.
[0019] In an aspect, the server constructs the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.
[0020] In accordance with another embodiment of the present invention, there is provided a system for assigning archetype to a plurality of users of a website. The system comprises at least a server configured to: administer an archetype indicator test to a first set of users of the website, acquire a first set of responses to the archetype indicator test from the first set of users, collate the first set of responses to determine a user classification proportion per archetype, determine a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website, obtain a digital footprint data of the plurality of users from a user profile database, construct a contextual scenario based on a sequence of activities of the user on the website and the digital footprint data of the user, map the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme, create an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors, and assign an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.
[0021] In an aspect, the server is configured to execute the archetype indicator test periodically on the user profile database.
[0022] In an embodiment, the server is configured to determine a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.
[0023] In an aspect, the server is configured to uniquely identify a user.
[0024] In an embodiment, the server is configured to: detect a first pattern within a session of the user on the website, detect a second pattern across multiple sessions of the user on the website; and determine the theme based on first and second patterns of the user across the multiple sessions of the user.
[0025] In an aspect, the server is configured to construct the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0026] Reference will be made to embodiments of the present invention, examples of which may be illustrated in the accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
[0027] The above and other objects, features, and advantages of the present invention will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:
[0028] Figure 1 is a proposed architecture block diagram elaborating the determination of archetypes in real time and the process of deploying archetype indicator test.
[0029] Figure 2 is an exemplary embodiment disclosing about the first party data match among the publishers and the brand.
[0030] Figure 3 is a detailed data flow diagram of the method to auto assign archetypes to the consumers.

DETAILED DESCRIPTION OF THE INVENTION
[0031] The embodiments herein provide a system and method for auto assigning archetypes to consumers, more particularly the present invention relates to a platform that enables auto-identifying and assigning the consumers into a plurality of archetypes in real time based on digital content engagement and consumer psychographics. Embodiments may also be implemented as one or more applications performed by stand alone or embedded systems.
[0032] The systems and methods described herein are explained using examples with specific details for better understanding. However, the disclosed embodiments can be worked on by a person skilled in the art without the use of these specific details.
[0033] Throughout this application, with respect to all reasonable derivatives of such terms, and unless otherwise specified (and/or unless the particular context clearly dictates otherwise), each usage of:
“a” or “an” is meant to read as “at least one.”
“the” is meant to be read as “the at least one.”
References in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0034] Hereinafter, embodiments will be described in detail. For clarity of the description, known constructions and functions will be omitted.
[0035] Parts of the description may be presented in terms of operations performed by a computer system, using terms such as data, state, link, fault, packet, and the like, consistent with the manner commonly employed by those skilled in the art to convey the substance of their work to others skilled in the art. As is well understood by those skilled in the art, these quantities take the form of data stored/transferred in the form of non-transitory, computer-readable electrical, magnetic, or optical signals capable of being stored, transferred, combined, and otherwise manipulated through mechanical and electrical components of the computer system; and the term computer system includes general purpose as well as special purpose data processing machines, switches, and the like, that are standalone, adjunct or embedded. For instance, some embodiments may be implemented by a processing system that executes program instructions so as to cause the processing system to perform operations involved in one or more of the methods described herein. The program instructions may be computer-readable code, such as compiled or non-compiled program logic and/or machine code, stored in a data storage that takes the form of a non-transitory computer-readable medium, such as a magnetic, optical, and/or flash data storage medium. Moreover, such processing system and/or data storage may be implemented using a single computer system or may be distributed across multiple computer systems (e.g., servers) that are communicatively linked through a network to allow the computer systems to operate in a coordinated manner.
[0036] In accordance with an embodiment of the present invention, a method for for assigning archetypes to a plurality of users of a website comprises: administering, by a server, an archetype indicator test to a first set of users of the website; acquiring, by the server, a first set of responses to the archetype indicator test from the first set of users; collating, by the server, the first set of responses to determine a user classification proportion per archetype; determining, by the server, a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website; obtaining, by the server, a digital footprint data of the plurality of users from a user profile database; constructing, by the server, a contextual scenario based on a sequence of activities of the user on the website and the digital footprint data of the user; mapping, by the server, the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme; creating, by the server, an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors; and assigning, by the server, an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.
[0037] In an aspect, the server is configured to execute the archetype indicator test periodically on the user profile database.
[0038] In an embodiment, determining, by the server, a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.
[0039] In an aspect, the method uniquely identifies a user.
[0040] In an embodiment, constructing the contextual scenario includes: detecting a first pattern within a session of the user on the website, and detecting a second pattern across multiple sessions of the user on the website; and wherein the server determines the theme based on first and second patterns of the user across the multiple sessions of the user.
[0041] In an aspect, the server constructs the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.
[0042] In accordance with another embodiment of the present invention, a system for assigning archetype to a plurality of users of a website comprises at least a server configured to: administer an archetype indicator test to a first set of users of the website, acquire a first set of responses to the archetype indicator test from the first set of users, collate the first set of responses to determine a user classification proportion per archetype, determine a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website, obtain a digital footprint data of the plurality of users from a user profile database, construct a contextual scenario based on a sequence of activities of the user on the website and the digital footprint data of the user, map the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme, create an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors, and assign an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.
[0043] In an aspect, the server is configured to execute the archetype indicator test periodically on the user profile database.
[0044] In an embodiment, the server is configured to determine a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.
[0045] In an aspect, the server is configured to uniquely identify a user.
[0046] In an embodiment, the server is configured to: detect a first pattern within a session of the user on the website, detect a second pattern across multiple sessions of the user on the website; and determine the theme based on first and second patterns of the user across the multiple sessions of the user.
[0047] In an aspect, the server is configured to construct the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.
[0048] Figure 1 is a proposed architecture block diagram elaborating the determination of archetypes in real time and the process of deploying archetype indicator test. This method includes conducting Archetype Indicator Tests (AITs) across all the digital consumers. After conducting the AITs across said consumers, a base archetype is determined which is known as a panel pool. Once the panel pools are determined then in real time, the method can assign any user/consumer an archetype. The Archetype Indicator Test (AIT) is a psychometric tool that once deployed, creates a panel pool. A panel pool is a number of group of individuals either determined by a brand which is the demand side or by a publisher which is the supply side as a first party data set. Once the panel pool is determined, it identifies their consumers or universe of consumer segment archetype. The basis for identifying the brand or the consumer segmentation is done by psychometric profiling. The AIT allows to assign an archetype in real time to other users/consumers. In real time, the method deploys psychometric profiling and in real time it gives output or media measurements by archetypes that means we get psychometric and demographic data both. The plurality of panel pools are associated with a plurality of nodes.
[0049] According to an exemplary embodiment, an ‘X’ company has conducted the archetype indicator test for its 2000 employees. Once the output has been determined by the AITs, the base archetype is defined that means panel pools are created in real time. In the panel pools, each panel pool becomes a node. For instance, ‘X’ company becomes Node 1, ‘Y’ company becomes Node 2 and in the similar manner a plurality of nodes are created. These plurality of nodes use the technology of blockchain to start collecting data and then effective outputs are generated with the help of machine learning and artificial intelligence. As panel pools is created for 2000 employees, this data for said employees is used for the other plurality of consumers. This method in turn makes the plurality of nodes stronger. Thus, the ‘X’ company pushes a plurality of consumer profiles that have been created in a panel pool by deploying the archetype indicator test. Based on the archetype indicator test, once it is deployed, the method can activate consumer profiles across the publisher side.
[0050] The method as disclosed herein allows segmentation of consumers on any platform where content is available. The method further assigns archetypes to a cohort of consumer profiles. These cohorts are converted and then mapped to IAB taxonomy or audience taxonomy and then in the programmatic world, the consumers can start online shopping and enable trading both on the brand side which is the demand side and the publisher side which is the supply side. In real time, the method is able to deploy and determine archetype to any user and categorize them in a cohort.
[0051] Figure 2 is an exemplary embodiment disclosing about the first party data match among the publishers and the brand. The first party data sets of the plurality of publishers are mapped to the plurality of brands. The user profile library is created by employing deep learning neural network machine learning stack with model training from reference data from industry bodies like Kantar, performance data from the campaigns.
[0052] Figure 3 is a detailed data flow diagram of the method to auto assign archetypes to the consumers. The method disclosed in the present invention includes steps of fusing, clustering, modelling and inference, and said steps of the method are applied at respective planes to arrive at the archetypes. The onboarding of the content publisher starts with administering the Archetype Indicator Tests (AIT) for a synthetic sample of users/consumers to the website. The data from these tests are collated to arrive at a user classification proportion per archetype. The size of the synthetic sample is subjected to the arrangement with the publisher and it might cover all the strata of the consumers.
[0053] The digital data footprints of the consumer are the primary sources of structured data, stratified, unified, classified, and stored in an easily retrievable mode per session. Further, the three forms of personality computing viz. APR, APP and APS are worked together through digital footprints and interplay with regard to content consumption and the reaction to the stimuli gathered from the available sources. Apart from the demographic information, the following data points are captured (the list is not limited to): type of the device, browser, operating system, and a user profile construction commences with each unique user with an assigned unique identifier. Each new user is assigned a default archetype proportionally in the ratios obtained in the first step. The repeated consumers are identified with the loyalty index, repeat frequency and other engagement parameters like number of outside shares, comments such as positive, negative and recency index.
[0054] The Landing Page (the first page of the visit in a session) and the time spent on it, and the referral URL play a significant role in determining the consumer’s interest.
[0055] Subsequently, the entire user navigation through the website gives rise to a scenario construction. The sequence of the events during the user navigation is added with weights depending on the bounce rates to give rise to the patterns. These patterns concerning each profile stored per unique session.
[0056] The contextual information and theme is extracted by Natural Language processing and will run through the theme determination stack, followed by the emotion determination stack, cognitive analysis stack, and by the sentiment analysis to arrive at the central theme (from the word cloud), emotion and cognitive index and the sentiment (positive, neutral and negative). Scenarios are enriched with the theme indicators. The type of the content such as textual, video, audio, infographics gives an additional dimension to user interaction affinities. The exit page of the session and the related bounce rate are also considered in the scenario construction. Thus, the contextual and scenario construction serves as a bridge for modelling the behavioural patterns from the heterogeneous personal data. Further, the demographics heavily influences scenario and contextual data.
[0057] The patterns are recognized within a session and across the sessions to arrive at a predominant content theme, emotion, sentiment, platform affinity, and cognitive factors. These are mapped to the existing patterns of the archetype determination skew ratios to arrive at the archetype stack with predominant, secondary, and other minor archetypal factors. The data from the industry bodies such as Kantar provides indicators on the metadata level to influence the working of the algorithm. Further, additional inputs from the user/consumer activity will emerge as the user interacts with the content by sharing the content in social media, commenting on the content in the comment box, more than one visit to the same content over sessions. Also, campaign data forms a significant input as it establishes the appeal of the creative and the section relevance and the creative type and archetype affinities.
[0058] The Archetype Indicator Tests (AIT) when run periodically in the publisher’s websites, will orient the distribution of the archetypes among the entire user profile database. They will act as a balancing factor in allocating the archetypes for the new users or visitors or consumers. This method covers a more extensive set of users/consumers. It classifies based on the patterns, and the combination of the two approaches (APR with APP and APS) will find itself an effective way of representation.
[0059] According to one of the preferred embodiments, the system and method thereof of the present invention may be implemented as a client server network.
[0060] According to one of the preferred embodiments, the system and method thereof of the present invention may be implemented over a cloud computing platform.
[0061] According to one of the preferred embodiments, a method is provided to auto-assign archetypal attributes to the consumers based on their visitation cycles on a particular website.
[0062] According to one of the preferred embodiments, a method is provided for classifying users as archetypes to provide rich psychographics and mindset information about the consumers that tend to use the website frequently or use the application or browse through social media channels that the publisher promotes with their content.
[0063] According to one of the preferred embodiments, the method lies in the deep learning of the patterns from feeding storage of big amount of data and the collation of several indexes. The data is derived to generate insights, tap the responses and infer the archetype.
[0064] According to one of the preferred embodiments, the data is normalized into the internal format from various groups of the data stream, and signals are identified to generate an effective base model.
[0065] According to one of the preferred embodiments, the method combines two powerful ways of classifying the data with APR, leading the way through panel pools developed in conjunction with IMRB (Kantar) and combining with APS.
[0066] According to one of the preferred embodiments, one of the critical outputs of the method is to work towards the identification framework for the consumers, which is an alternative for cookie-based user tracking or targeting.
[0067] According to one of the preferred embodiments, the system is configured to perform a psychographic segmentation of the digital consumers which is based on their answers to a series of questions about their preferences, needs and motivations. By using the psychographic segmentation, the marketers may identify patterns of behaviour, interest and preferences to form distinctive archetypes.
[0068] In some embodiments, the disclosed techniques can be implemented, at least in part, by computer program instructions encoded on a non-transitory computer-readable storage media in a machine-readable format, or on other non-transitory media or articles of manufacture. Such computing systems (and non-transitory computer-readable program instructions) can be configured according to at least some embodiments presented herein, including the processes shown and described in connection with Figures.
[0069] The programming instructions can be, for example, computer executable and/or logic implemented instructions. In some examples, a computing device is configured to provide various operations, functions, or actions in response to the programming instructions conveyed to the computing device by one or more of the computer readable medium, the computer recordable medium, and/or the communications medium. The non-transitory computer readable medium can also be distributed among multiple data storage elements, which could be remotely located from each other. The computing device that executes some or all of the stored instructions can be a microfabrication controller, or another computing platform. Alternatively, the computing device that executes some or all of the stored instructions could be remotely located computer system, such as a server.
[0070] Further, while one or more operations have been described as being performed by or otherwise related to certain modules, devices or entities, the operations may be performed by or otherwise related to any module, device or entity.
[0071] Further, the operations need not be performed in the disclosed order, although in some examples, an order may be preferred. Also, not all functions need to be performed to achieve the desired advantages of the disclosed system and method, and therefore not all functions are required.
[0072] While select examples of the disclosed system and method have been described, alterations and permutations of these examples will be apparent to those of ordinary skill in the art. Other changes, substitutions, and alterations are also possible without departing from the disclosed system and method in its broader aspects.

,CLAIMS:

1. A method for assigning archetypes to a plurality of users of a website, comprising:
administering, by a server, an archetype indicator test to a first set of users of the website;
acquiring, by the server, a first set of responses to the archetype indicator test from the first set of users;
collating, by the server, the first set of responses to determine a user classification proportion per archetype;
determining, by the server, a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website;
obtaining, by the server, a digital footprint data of the plurality of users from a user profile database;
constructing, by the server, a contextual scenario for based on a sequence of activities of the user on the website and the digital footprint data of the user;
mapping, by the server, the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme;
creating, by the server, an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors; and
assigning, by the server, an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.

2. The method as claimed in claim 1, wherein the archetype indicator test is executed periodically by the server on the user profile database.

3. The method as claimed in claim 1, comprising determining, by the server, a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.

4. The method as claimed in claim 1, wherein the method uniquely identifies a user.

5. The method as claimed in claim 1, wherein constructing the contextual scenario includes:
detecting a first pattern within a session of the user on the website, and
detecting a second pattern across multiple sessions of the user on the website; and
wherein the server determines the theme based on first and second patterns of the user across the multiple sessions of the user.

6. The method as claimed in claim 1, wherein the server constructs the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.

7. A system for assigning archetypes to a plurality of users of a website, said system comprising at least a server configured to:
administer an archetype indicator test to a first set of users of the website,
acquire a first set of responses to the archetype indicator test from the first set of users,
collate the first set of responses to determine a user classification proportion per archetype,
determine a default archetype based on the user classification proportion, wherein the default archetype is assigned to new users of the website,
obtain a digital footprint data of the plurality of users from a user profile database,
construct a contextual scenario based on a sequence of activities of the user on the website and the digital footprint data of the user,
map the contextual scenario of the consumer to one or more predetermined archetypes for determining a theme,
create an archetype stack based on the theme, said archetype stack including a plurality of archetypal factors, and
assign an archetype to each of the plurality of consumers based on the user classification proportion and the determined archetype stack.

8. The system as claimed in claim 7, wherein the server is configured to execute the archetype indicator test periodically on the user profile database.

9. The system as claimed in claim 7, wherein the server is configured to determine a personality of each user based on at least one of: Automatic Personality Recognition, Automatic Personality Perception, and Automatic Personality Synthesis.

10. The system as claimed in claim 7, wherein the server is configured to uniquely identify a user.

11. The system as claimed in claim 7, the server is configured to:
detect a first pattern within a session of the user on the website,
detect a second pattern across multiple sessions of the user on the website; and
determine the theme based on first and second patterns of the user across the multiple sessions of the user.

12. The system as claimed in claim 7, wherein the server is configured to construct the contextual scenarios based on one or more of: All Elements First technique, Elements Completion technique, and Multiple Scenario Detection technique.