DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD TO SOLVE USER-CENTRIC CHALLENGES USING ANCIENT TEXT INTELLIGENCE ONTOLOGY

Applicant:
Shashidhar Venkatsatya Govindraju
A natural person, resident of India, having address:
B-604, Aklvya CHS Ltd,
Plot 69 J/K/D, Sector 21, Kharghar,
Navi Mumbai, Maharashtra,
PIN 410210, India

The following specifications particularly describe the proposed invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[001] The present application claims priority from Indian provisional patent application number 202121046801, filed on October 13, 2021. The entire content of the abovementioned application is incorporated herein by reference.

TECHNICAL FIELD

[002] The invention generally relates to the field of using knowledge derived from ancient text to solve or deal with user-centric challenges. More particularly, the invention relates to a system and method for recommendation and suggestion of contextual information from ancient scriptures and teachings therein to solve or deal with user-centric challenges.

BACKGROUND

[003] Computing has become deeply embedded in the structure of modern society and is seen as one of the gateways for user functions. Computing tools have enabled tasks or functions that weren’t possible so many years ago. Through computing, most of users’ social infrastructures such as communication, knowledge sharing etc. are operated and managed. Existing computing systems have little appreciation or understanding of user’s overall context and as a result, any response by the computing system will attempt to accommodate a wide range of user contexts and is thus, unlikely to be optimal to specific context of the user.

[004] Lack of knowledge about the required context of the users and lack of required information and practices to be followed are two main reasons today that limit the ability of computing systems and the related machines to address many user challenges. The former reason results in the lack of understanding of user-challenges (problems) concerning a user or user community.

[005] Present dialogue-based systems are also not able to address this difficulty due to one or more of the following reasons: (i) lack of computational system’s intelligence to ask or frame appropriate questions, (ii) user’s inability to answer the questions presented by the computational system, and (iii) inability of the user to provide information in a format that is decipherable by the computational systems. Said dialogue-based systems having answering agents, then find it difficult to address the user’s question. In particular, since existing computing systems lack adequate information about a user’s current context, they cannot provide information appropriate to that context or anticipate likely changes in the context.

[006] Amongst the plethora of contextual information identified and understood, the system’s knowledge of the user’s self is the missing contextual information, causing inadequate understanding of the user’s question or context by the computing systems. Knowledge of the self may mean several things represented by a variety of expressions such as thoughts, cognitive states, mental states, emotional states, personality, behavior, preferences, health status, lifestyle and unconscious actions. The imprecise and long list of such expressions, with overlapping interpretations and lack of means to measure them, are the root causes for users not being able to provide such contextual information to the answering agent and the answering agent’s limited ability to understand user query. Such contextual information is however required for further technological advancement.

[007] Ancient texts, scriptures and practices are evidences of thought, language, society and history of past civilizations and development of user communication. However, limited availability of such knowledge hinders the ability to search and understand the documented knowledge that is available offline. The knowledge from the ancient texts, scriptures and practices, once understood, needs to be presented in a conducive searchable format. One of the alternates may be to automate the knowledge and learnings of the ancient texts.

[008] The real gap in automation lies in either the knowledge not being understood or those who understand the knowledge are not within the realm of technology and automation. In either case, the automation efforts would require the knowledge to be represented in a machine-understandable form. Even if the knowledge were to be presented into machine-understandable form, it would simply be an abstract automation as it would lack the intelligence and intellect that needs to be added to the system to understand the challenge-question posed or sent by the user. In prior art we have two generations of Artificial Intelligence (AI) systems namely expert systems (first generation AI systems) and machine learning systems (second generation AI systems) that are used for automation.

[009] The expert systems gather intelligence by learning from structured data obtained from experts. Expert systems use their knowledge and rules incorporated within, for responding to one or more user inputs by telling what to do and how to do. The machine learning systems gain intelligence by learning from flat files given to the system. Machine learning systems learn from large data and make data-driven decisions for responding to one or more user inputs.

[0010] In some instances, the expert systems and machine learning systems include question answering (“QA”) systems, which can generally sense and respond to aspects of the setting in which they are used. The user context is not a new term for QA systems. QA systems consider context as the information about a user or the environment within which a user makes a query. However, addressing user-challenges may require finding contextual details, such as the user’s cognitive states, emotional states, physical states, preferences, unconscious actions, capabilities, achievements, lifestyle, successes, failures of the users or other relevant user entities, which is a much more difficult task.

[0011] Various machine learning models with deep learning based multi-model approaches have received attention due to prediction of personality traits. While personality forms a part of user-context, it becomes the most plausible approach to leverage the existing personality detection approaches to find the foundation of user-context by answering multiple questions. Automated personality detection methods use a large number of behavioral profiles available from various computational datasets of chosen personality models for a given application. For example, such an application may try to find the user’s suitability for a job profile, driving behavior, or risk of committing a terrorist act.

[0012] The meaning and objective of finding the user’s context is very different for personality detection and for answering challenge-questions. Therefore, existing approaches of personality detection using personality models, available datasets, and behavioral profiles may not be of much use to an open-domain question-answering system for answering the challenge-questions. Though, these models use large and diverse data for personality detection and prediction applications, yet the focus towards understanding of user-context and solving user challenge is very limited and needs deeper exploration.

[0013] It is to be noted that development of expert systems and machine learning systems for providing context-based solution to a challenge question requires additional computational efforts, which have not been rendered in the current and conventional systems. In continuation, the existing computational systems are limited to specific domains, platforms and requirements to have data for each and every user query in their databases. There have been instances where QA systems, that were developed over the years, failed due to non-specific answers to user queries, unmitigated and unsupervised learning mechanisms leading said QA systems to digress from the very purpose for which they were built.

[0014] In view of the foregoing, there is a long felt need for a system and method for exploring newer models and approaches to find necessary foundations of the user’s context for addressing the new category of questions, namely challenge-questions (described in detail later).

OBJECTIVES

[0015] The principal objective of the present invention is to provide a system and method for solving user-centric challenges using knowledge derived from ancient texts and scriptures and learnings therein.

[0016] Another objective of the present invention is to provide a method and system that helps in obtaining the required users’ context for providing solutions to user-challenges.

[0017] Another objective of the invention is to provide machine-understandable knowledge representation of required knowledge models, practice models, and insight database derived from ancient texts with the help of knowledge experts and practitioners and support in improving said models and databases through computational system learning in real-time and improving computational accuracy.

[0018] Another objective of the present invention is to provide a method and system that helps in operationalizing knowledge models derived from ancient texts and scriptures and aids in improving implementation of the derived knowledge models and improving computational efficiency.

[0019] Another objective of the invention is to implement a knowledge model having elements including Senses, Mind, Intelligence and Soul (SMIS) to derive knowledge and understand user context using knowledge derived from ancient texts.

SUMMARY

[0020] This summary is provided to introduce aspects related to systems and methods for solving user-centric challenges using ancient text intelligence ontology, and the aspects are 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.

[0021] In view of the foregoing para, an embodiment herein provides a computer implemented method for solving user-centric challenges using ancient text intelligence ontology. In an embodiment, a computer implemented method for solving user-centric challenges using the learnings and knowledge derived from ancient texts and scriptures. The method may include receiving a request for authentication of user device from the user wherein the user is authenticated basis matching of the login credentials entered by the user with the pre-stored login credentials, selecting and assigning a pre-stored knowledge model to the user wherein the knowledge model is derived from one or more ancient texts and scriptures, analyzing a challenge question entered by the user and obtaining context from the user related to the challenge question, fetching one or more insights basis the analysis of challenge questions, context obtained from the user and knowledge model mapped to the user, wherein one or more insights are extracted from a system repository containing a plethora of insights derived from one or more ancient texts and scriptures, and providing an engineered solution to the user containing one or more insights and one or more practice models in a format understandable to the user, wherein one or more practice models are derived from one or more ancient texts and scriptures and are stored in system repository.

[0022] In an embodiment, a computer implemented system for solving user-centric challenges using ancient text intelligence ontology is provided. The system may include one or more processors and a memory storing processor executable instructions that, when executed by one or more processors, configure the one or more processors to: authenticate a user basis login credentials of the user; select and assign a knowledge model pre-stored in a system repository to the user, wherein the knowledge model is derived from one or more ancient texts and scriptures; analyze a challenge question related to the user and obtaining context from the user related to the challenge question; fetch fetching one or more insights basis the analysis of challenge question, context obtained from the user and the knowledge model mapped to the user, wherein one or more insights are extracted from the system repository containing a plethora of insights derived from one or more ancient texts and scriptures; and provide an engineered solution to the user containing one or more insights and one or more practice models in a format understandable to the user, wherein one or more practice models are derived from one or more ancient texts and scriptures and are stored in the system repository.

[0023] It should be appreciated by those skilled in the art that any block diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code and like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.

BRIEF DESCRIPTION OF DRAWINGS

[0024] The foregoing summary, as well as the following detailed description of preferred embodiments, is better understood when read in conjunction with the appended drawings. There is shown in the drawings example embodiments, however, the invention is not limited to the specific methods and architecture disclosed in the drawings.

[0025] Figure 1 illustrates a network environment implementing a system to solve or deal with user challenges using ancient texts, in accordance with an embodiment of the present subject matter.

[0026] Figure 2 illustrates components of the system to solve or deal with user challenges using ancient texts, in accordance with an embodiment of the present subject matter.

[0027] Figure 3 illustrates a method for solving or dealing with user challenges using ancient texts, in accordance with an embodiment of the present subject matter.

DETAILED DESCRIPTION

[0028] Ancient texts and scriptures hold a vast array of knowledge ranging from areas of science including astronomy, medicine, aeronautics, mathematics and development of the universe. It has been accepted that western criteria are not the sole benchmark by which other knowledge systems should be evaluated. While the usage of ‘ancient’ often implies ‘primitive’ or ‘outdated’, many of the ancient science and technologies described or detailed in the ancient texts and scriptures were quite advanced, compared to present-day standards and better adapted to solve most of the present-day challenges and needs than their modern alternatives.

[0029] The importance of knowledge and learning contained in ancient texts attracted researchers around the world to explore knowledge preservation, digitalization and representation for computational analysis. The real-exploitation of ancient texts in different sectors have already begun. For example, the Indian army has been at the forefront in this regard and have inculcated the concepts of Indian ancient texts and scriptures in conjunction with an understanding of contemporary situations and battle space architecture. The real exploitation of ancient texts in technology for a better life, however, is yet to begin. Time-tested knowledge in ancient scriptures, though, meant for leading a better life, has not been exploited much as of yet.

[0030] Ancient texts and scriptures were originally oral and were passed down through memorization through generations until they were codified. There are some ancient texts and scriptures in the world, whose knowledge of interpretation and understanding of language has been lost over time. However, there are certain ancient texts and scriptures, which are well preserved and referred to by different races including ancient Indian texts and scriptures including Vedas, Puranas, Upanishads and international ancient texts and scriptures including Bible, Quran, Book of Mormon, Torah and the like. For clarity, it is stated that within the scope of this invention, ancient text means Indian, international texts and scriptures, their derivatives or a combination thereof. For explaining the invention through example embodiments, the facets of invention have been described to have been derived from Bhagavad-Gita, which is an Indian scripture. However, it is to be understood that the facets of the invention may be derived from one or more of Indian ancient texts and scriptures and international ancient texts and scriptures, their derivatives or a combination thereof.

[0031] Many ancient texts and scriptures have the knowledge and learning contained in the form of verses, lessons and the like. Indian Vedas also include Sanskrit hymns and verses, which are translated and interpreted to derive learnings by the experts. Bhagavad-Gita (an ancient Indian scripture) is based on learning the knowledge of self through question answer format. It is complete and available ancient text which is solely built on the question answering system in the world.

[0032] Computational systems by combining the power of artificial intelligence, machine learning and linguistics try to learn and understand user needs. While said systems are able to understand and provide multiple solutions to some of the challenges or problems faced by users. However, these systems at present do not provide appropriate and adequate solutions to some of the problems which are faced by users. There have been instances during the course of user interacting with the computational systems wherein the user query, based on a real-life situation, has been unsatisfactorily answered or not answered at all. Said user-query triggered by user-real-life challenge shall be referred to as “Challenge Question”. It is to be noted that the challenge question may not necessarily be triggered by real-life challenges and may include any kind of challenge that is faced by user, for which the user feels the need to interact or interacts with the computational systems. For the sake of clarity, let’s consider the example of a user asking the challenge question “How to learn guitar” to the computational system having a question-answering (QA) agent. The QA agent will generally be able to answer this query using available knowledge sources, with the scope to refine or personalize the response using contextual user inputs. For the second instance of user asking the question “Why am I not able to learn guitar?” suggesting the problem faced by the user in learning the guitar instrument and approaches to overcome the challenges. The QA agent, in second instance, may offer a list of reasons why majority of users are not able to learn guitar along with motivational tips. The QA agent may need some defined mechanism for converging to one of the reasons, applicable to the particular user posing the second question. It becomes clear that answering second question by QA agent requires additional details including but not limited to user context, which was not the case with first example. The second question is an example of “Challenge Question”. It is to be noted that these challenge questions may be user-based, group-based or organizational based. In an embodiment, the challenge questions may be based on issues faced basis user to user interaction, peer to peer interactions and user to organizational interactions.

[0033] Additionally, it isn’t enough that users pose questions, the question answering systems should be able to understand the problem as precisely as possible. The quality of problem understanding by computational systems will have direct consequence on the quality of the solution.

[0034] The present subject matter discloses aspects related to solving or dealing with user-challenges presented in the form of challenge questions through an analytical knowledge framework having machine learning capabilities. In an implementation, the present subject matter may include a system that facilitates solving or dealing with user-challenges presented in the form of challenge questions by using knowledge models, insights (stored in insight database), and practice models derived from the ancient texts and scriptures. In an implementation, the system may derive the contextual information associated with the challenge question posed by the user basis the knowledge framework. In an implementation, the system may facilitate solving the user challenges presented in the form of challenge questions for an individual user application or multiple user applications supported by the system. The applications may include, but are not limited to fields or areas where the system is in constant or intermittent communication with the user to understand its needs and provide output or solutions.

[0035] These and other advantages of the present subject matter would be described in greater detail in conjunction with the following figures. While aspects of systems and methods described below for solving or handling challenge questions can be implemented in any number of different computing systems, environments and/or configurations, the embodiments are described in the context of the following exemplary system.

[0036] Figure 1 illustrates a network environment (100) of a system (102) to solve user challenges using ancient texts and scriptures in accordance with an embodiment of the present subject matter. The disclosed system (102) is capable of providing solutions to user-challenges presented in the form of challenge questions.

[0037] Although the present disclosure is explained considering that the system (102) is implemented on a server, it may be understood that the system (102) may also be implemented in a variety of computing systems (104), such as a laptop computer, a desktop computer, a notebook, a workstation, a cloud-based computing environment and the like. It will be understood that the system (102) may be accessed through one or more devices (106-1), (106-2) …(106-N), collectively referred to as user devices (106) hereinafter, or applications residing on the user devices (106). Examples of the user device (106) may include, without limitations, a portable computer, a personal digital assistance, a handheld device, a smartphone, a tablet computer, a workstation and the like. The user devices (106) are communicatively coupled to the system (102) through a network (108).

[0038] In an embodiment, the network (108) may be a wireless or a wired network or a combination thereof. In an embodiment, the network (108) can be implemented on a computer network, as of the different types of networks, such as virtual private networks (VPN), intranet, local area network (LAN), wide area network (WAN), the internet and such. The network (108) 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 including Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP) and Wireless Application Protocol (WAP) to communicate with each other. Further, the network (108) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network device within the network (108) may interact with the system (102) through communication links.

[0039] As discussed above, the system (102) may be implemented in a computing device (104), such as a hand-held device, a laptop or other portable computer, a tablet computer, a mobile phone, a PDA, a smartphone and a desktop computer. The system (102) may also be implemented in a workstation, a mainframe computer, a server and a network server. In an embodiment, the system (102) may be coupled to a data repository, for example, a system repository (110). The system repository (110) may store data processed, received and generated by the system (102). In an alternate embodiment, the system (102) may include the system repository (110).

[0040] In an implementation, the system (102) may be coupled to a system repository (110). In an implementation, the system repository (110) may include a knowledge repository (112), insight repository (114) and solution repository (116). It will be understood that although the system repository (110) is shown external to the system (102), the system repository (110) may be provided internal to the system (102). In an embodiment, the system repository (110) may be provided as a relational database and may store data in various formats known in prior art. Further it will be understood that the system repository (110) may be provided as one or more operational database.

[0041] In an implementation, the knowledge repository (112) may include data related to machine implementable models derived from the ancient text, scriptures, their derivates or a combination thereof with the help of experts and practitioners. In an example embodiment, the knowledge repository (112) may include data related to machine implementable model(s), referred to as knowledge model, derived from Indian scripture Bhagavad-Gita. In an implementation, one or more knowledge models may be derived from Indian scripture Bhagavad-Gita by the knowledge expert and practitioner. In an implementation, the knowledge model derived is Senses, Mind, Intelligence and Soul (SMIS) knowledge model. The machine implementable SMIS knowledge model includes four components including Senses, Mind, Intelligence and Soul, which are mapped on four operating planes in the SMIS knowledge model. These four components may be treated as four operating planes with Senses at the lowest plane and Soul at the topmost plane. The order of placement from bottom to top in the operating plan may be Senses, Mind, Intelligence and Soul. For the purpose of clarity, the knowledge repository (112) may contain data related to Senses, Mind, Intelligence and Soul Model (SMIS Model) including data related to classifiers, quiz(es), survey instruments mapped to the SMIS model for facilitating the mapping of the SMIS model components to the user including mapping of the operating planes. These classifiers, quiz(es) or survey instruments are generic in nature and requisition of additional information or their application is done through steps known in the art.

[0042] In an implementation, the insight repository (114) may include data in the form of insights derived from ancient text, scriptures, derivatives or a combination thereof, with the help of knowledge experts and practitioner, forming a database referred to as insight database. In an example embodiment, the insight database stored in insight repository (114) may include data in the form of insights derived from Indian scripture Bhagavad Gita. The insights may be presented and stored in the form of rules derived from the Bhagavad-Gita and are mapped to SMIS model. For each operating plane of the SMIS model, one or more insights are mapped. In an example embodiment, the insight database stored in insight repository (114) may contain a number of insights mapped to Senses, Mind, Intelligence and Soul operating planes of the SMIS model. The insights mapped at each plane of SMIS model are exclusive in nature and may not overlap with insights mapped to another plane.

[0043] In an implementation, the solution repository (116) may be configured to store data related to solutions or recommendations towards a user query. These solutions or recommendations are derived from ancient text, scriptures, derivatives or a combination thereof, with the help of experts and practitioners. In an example embodiment, the solution repository (116) may include data in the form of solutions, recommendations or practices to be followed by the user in response to a challenge question, derived from Indian scripture Bhagavad-Gita. In an example embodiment, the solution repository (116) may contain solution or recommendations based on threefold miseries of material existence, four defects of a conditioned soul, three modes of material nature and the like, derived from Bhagavad-Gita, which may be types of practice models stored in the solution repository (116). It is to be noted that these are not abstract models (or concepts) but rather machine implementable practice models designed basis the knowledge of ancient texts (including Bhagavad-Gita) with the help of knowledge experts and practitioners. The practice models stored in the solution repository (116), are derived by the knowledge experts and practitioners from the ancient texts, scriptures and their derivatives or a combination thereof. In an embodiment, the system (102) may automatically derive the practice models from ancient texts or improve upon the existing practice models stored in the solution repository (116).

[0044] In an example embodiment, the threefold miseries practice model stored in the solution repository (116) is detailed herein. The practices models are derived by the knowledge experts and practitioners basis the knowledge of the ancient texts and scriptures. In an embodiment, the practice models stored in solution repository (116) are derived from the Indian scripture Bhagavad-Gita. In an embodiment, the practice models may include standalone practice model based on single verse of the Bhagavad-Gita or multifold practice model derived from multiple verses. The solution repository (116) presents the practice models to the system (102) as prescriptive part of the solution to the challenge question posed by the user to the system (102). In an embodiment, the solution repository (116) may be configured to store the practice models with suitable classifiers or rules or forms of system intelligence known in the art. For the sake of clarity, the data stored in knowledge repository (112) supports the system (102) in finding the source of the issues described in challenge question, the insight repository (114) and solution repository (116) helps in finding the solution to the challenge question.

[0045] It is to be noted that machine implementable models can learn in supervised mode or be self-learning models using unsupervised learning and trained on a normal behavior of different aspects of the system, for example, user activity and user activity associated with the system. It is to be noted that a single model may include aspects of supervised learning and self-learning using unsupervised learning. In an embodiment, the knowledge model (described above) includes both aspects of supervised as well as unsupervised learning. In an embodiment, the process of self-learning may be initiated by adding new input examples, new data received by the system, new user interaction and the like. Once the knowledge model has gone through modes of supervised and unsupervised self-learning (including rule based self-learning or machine learnt self-learning), a hybrid self-learning system (also known as hybrid machine learning system) for knowledge model may be explored by the system when a large data set is gathered and processed by the system.

[0046] With reference to knowledge model, the unsupervised learning aspect include selection of one of the knowledge models from the knowledge repository. The supervised learning aspects include incorporating new knowledge models into knowledge repository and mapping of overall operating plane (“OOP”) of a selected model to the user. As stated in the foregoing para, once the knowledge model has gone through mode of supervised learning of mapping OOP to the user and has gathered a large set of data, it may switch to hybrid self-learning mode (also known as hybrid machine learning system).

[0047] In an embodiment, the practice model (described above) includes both aspects of supervised as well as unsupervised learning. In an embodiment, the process of self-learning may be initiated by adding new input examples, new data received by the system, new user interaction and the like. Once the practice model has gone through modes of supervised and unsupervised self-learning (including rule based self-learning or machine learnt self-learning), a hybrid self-learning system for practice model may be explored by the system when a large data set is gathered and processed by the system.

[0048] With reference to practice model, the unsupervised learning aspect include selection of one of the practice models from the practice repository. The supervised learning aspects include recommending one or more practices to the user and incorporation of new practice models into practice repository. It will be appreciated by one skilled in that art that the process may be a continuous loop. As the process runs over time, the machine learning model may self-improve as more data is gathered and processed and become a hybrid self-learning model (hybrid machine learning system).

[0049] In an embodiment, the insight database stored in insight repository (114) may be updated by the system through self-learning mechanism associated with the insight module. The self-learning mechanism associated during initial cycles of data processing may include aspects of supervised learning. Once a number of cycles of data processing gets completed and a large set of data is collected, the insight module is programmed or designed to switch to unsupervised self-learning mechanism. In an embodiment, the insight module may be configured to switch to hybrid self-learning mechanism based on hybrid machine learning. It will be appreciated by one skilled in that art that the process may be a continuous loop.

[0050] The self-learning knowledge, practice models and insight database are updated when new input data is received and/or processed, that is deemed within limits of normal user and system behavior. In an embodiment, the knowledge models, practice models and insight database may be paired with a self-learning neural network for an even more advanced analytical approach and to improve or optimize the learning mechanism. In an embodiment, machine learning and cognitive computing may be implemented to decrease knowledge models & practice models learning and development time and enable self-learning knowledge model and practice models and their analytics. In an embodiment, machine learning and cognitive computing may be implemented to decrease time needed to update the insight database and enable self-learning and analytics.

[0051] It is to be noted that the knowledge models and practice models derived from ancient texts and scriptures are not limited to expert systems rather are based on machine learning models with capabilities to learn and improve in real-time. A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the system (102) includes at least one of a machine learning system.

[0052] The network environment (100) supports various connectivity options such as BLUETOOTH®, USB, ZigBee and other cellular services. The network environment (100) enables connection of devices (106) with the system (102) and accordingly with the system repository (112) using any communication link including internet, WAN, LAN, and the like. In an exemplary embodiment, the system (102) may be implemented to work as a loosely coupled device to a smart computing environment. The components and functionalities of the system (102) are described further in details with references to Figure 2 and Figure 3.

[0053] Referring now to Figure 2, the system (102) is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system (102) may include at least one processor (202), an input/output interface (204), and a memory (206). In an embodiment, interface (204) may further comprise extended interfaces for facilitating the users to access the system (102) simultaneously. The at least one processor (202) may be implemented as one or more micro-processors, microcomputers, micro-controllers, digital signal processors, CPUs, state machines, logic circuitries and/or any devices that manipulate signal based on operational instructions. Among other capabilities, the at least one processor (202) is configured to fetch and execute computer readable instructions stored in the memory (206).

[0054] The I/O interface (204) may include a variety of software and hardware interfaces for example, a web interface, a graphical user interface and the like. The I/O interface (204) may allow the system (102) to interact with a user directly or through a user device (106). The I/O interface (204) may enable the system (102) to communicate with other computing devices such as web servers and external data servers. The I/O interface (204) can facilitate multiple communications within a wide variety of networks and protocol types including wired networks, for example, LAN, cable etc., and wireless networks such as WLAB, cellular or satellite. The I/O interface (204) may include one or more ports for connecting a number of devices to one another or to another server. In an embodiment, the interface (204) may provide a set of accessibility features that make it easier for the users with disabilities to use the system (102).

[0055] The memory (206) may include any computer-readable medium known in the art including SRAM) and dynamic random-access memory (DRAM), and/or nonvolatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory (206) may include modules (208) and data (210).

[0056] The modules (208) include routines, programs, objects, components, data structures etc. which perform particular tasks, functions or implement particular abstract data types. In one implementation, the modules (208) may include an authentication module (212), a knowledge module (214), insight module (216), solution module (218) and other module (220). The other module (220) may include programs or coded instructions that supplement applications and functions of the system (102).

[0057] The data (210) may amongst other things, serves as a repository for storing data processed, received, and generated by one or more modules (208). The data (210) may include a data store (222) and other data (224). In one embodiment, the data store (222) may store user data including user information like login credentials, transaction history and other associated data. The other data (224) may include data generated as a result of the execution of one or more modules in the other modules (220).

[0058] In one implementation, at first, a user may use the user device (106) or the I/O interface (204) to access the system (102). The user may register themselves using the I/O interface (204) in order to use the system (102).

[0059] In an embodiment, the authentication module (212) may be configured to determine the authentication status of the user by comparing the user credentials entered into the system (102) by the user through the user device (106) or through I/O interface (204) with the user credential stored in data store (222). Upon receipt of a request from the user for accessing the system (102), the authentication module (212) prompts the user to log-in to the system (102). The user may log-in, for example, by entering a user credential, for instance, name and password. In another embodiment, the user credentials may include bio-metrics, face recognition and the like. The authentication module (212) determines the authentication status by comparing entered credentials with the user credentials stored in the data store (222). Upon a successful match, the authentication module (212) may allow the user to access the system (102).

[0060] In an embodiment, the knowledge module (214) may be configured to aid the user in providing the challenge question to the system (102). The user may enter the details of the challenge question through the I/O interface (204) or through the user device (106). In an embodiment, the user may enter the challenge question in a variety of forms including a sequence of text characters, sentences, paragraph, image, audible content and the like. It is to be noted that the present system (102) may be configured to have capabilities to allow differently/specially abled users to access the systems through methods or mechanisms known in the art.

[0061] In another embodiment, the knowledge module (214) may be further configured to analyze the challenge questions and contents thereto. The knowledge module (214) may be configured to select and assign one of the knowledge models stored in the knowledge repository (112). In an embodiment, the knowledge module (214) may select and assign the SMIS knowledge model stored in the knowledge repository (112). The selection and assignment of one of the knowledge models by the knowledge module (214) is done basis the methods known in the art. In an embodiment, the selection and assignment of the knowledge model may be done through a customized method, which is beyond the scope of this invention.

[0062] In another embodiment, the knowledge module (214) may be further configured to select and assign one of the operating planes of the SMIS knowledge model to the user. The knowledge module (214) may communicate with the knowledge repository (112) for mapping one of the operating planes of the SMIS knowledge model to the user by utilizing one of the classifiers, quiz(es), survey instruments mapped to the SMIS model. In an embodiment, the assignment of an operating plane to the user is done basis the user information existing or available in the system repository (110) or memory (206) and the response(s) of the user to the survey instruments. The assignment of one of the operating planes of the SMIS knowledge model to the user is done after analyzing the response of the user to the survey instruments wherein each entry in the survey instrument is linked with a scoring mechanism. The analysis of user response to survey instruments and gradually mapping operating plane of the SMIS knowledge model, is beyond the scope of this invention. In an embodiment, the mapping of one of the operating planes to the user is done through methods known in the art. It to be noted that knowledge module (214) determines the overall operating plane (OOP) of the user through assigning one of the elements of the SMIS model to the user.

[0063] In an embodiment, the knowledge module (214) may face the challenge of identifying attributes in the user causing the knowledge module (214) to face a tie-breaking situation towards determining the OOP of the user. Basis the analysis of user response to survey instruments, the knowledge module (214) may identify identical scoring for two OOPs of the SMIS knowledge model for a user. The knowledge module (214), in such an instance, may be configured to assign the OOP to the user by using tie-breaking mechanisms known in the art. It is to be noted that the knowledge module (214) will assign only one OOP of the SMIS knowledge model to the user. However, it is possible to have different OOP assigned to the same user depending on the response of user to survey instruments, demonstrating the growth of user.

[0064] In an embodiment, the mapping of OOP of the SMIS knowledge model to the user may be done without user prompting the system (102) with challenge question. In an embodiment, the knowledge module (214) may be configured to map the user with suitable OOP of the SMIS knowledge model and share the details of the OOP mapped to the user through I/O interface (204) or through the user device (106). Such a feature of the system (102) may be exploited for identifying the personality of the user or a group of users.

[0065] Further, the knowledge module (214) may be configured to analyze the challenge questions and contents thereto. The analysis of the challenge question depends on the variables including challenge question type, contents of the challenge question, length of the challenge question and the like. In an embodiment, the knowledge module (214) may be configured to probe the user for additional inputs through various methods known in the art. In an implementation, the knowledge module (214) may probe the user through sub-queries, engagement with QA agent to capture additional information to gain required understanding of the input text provided by the user. This analysis is done basis the methods known in prior art including natural language understanding and the like. It is to be noted that the knowledge module (214) probes the user for additional inputs in the event that the system (102) faces challenge in analysis due to reasons including incomplete nature of the challenge question entered by the user, challenge question presented to the system (102) by the user in non-legible format and the like.

[0066] In an embodiment, the knowledge module (214) may be further configured to probe the user with instruments to find the user’s contextual details required by the system (102) to provide the solution. In an embodiment, the instruments may be in the form of survey, quiz, visuals with necessary probes and sensory instruments to capture the additional details. The knowledge module (214) may be configured to capture the response of the user when probed with the instruments stored in the knowledge repository (112). It is to be noted that said instruments stored in the knowledge repository (112) are designed and incorporated in the system (102) with the help of knowledge experts and practitioners.

[0067] In an embodiment, the insight module (216) may be configured to fetch one or more appropriate insights basis the OOP of the SMIS model mapped to the user by the knowledge module (214) and the response of the user to the probing instruments, detailed in para [0066]. In an example embodiment, the insight module (216) fetches one or more appropriate insights basis the OOP mapped out of the SMIS model and response of the user to instruments presented to the user to capture additional contextual details. In an embodiment, the insight module (216) may communicate with the insight repository (114) for fetching the insight and the data related to insights stored therein. The fetching of appropriate insights for a challenge question with the mapped OOP and response of user to the probing instruments of the user, is done through methods, which are beyond the scope of this invention. In an embodiment, the fetching of appropriate insights for a challenge question may be done through methods known in the art.

[0068] In an embodiment, the solution module (218) may be configured to engineer the solution to a user provided challenge question. The solution module (218) derives the solution basis the output of the knowledge module (214) and insight module (216) and engineers it to be in a format which is understandable to the user. Additionally, the solution presented by the solution module (218) includes the recommendations of the system (102) in the form of practice models, which may enable to overcome the issue contained within the challenge question.

[0069] In an implementation, the system (102) may store the solution for each challenge question and may use the stored solutions to create data in the solution repository (116) and to learn the context for providing solution to subsequent challenge questions (which may be similar in nature). In an embodiment, the system (102) may store the solution for each challenge question posed by the user to compare the progress or evolution of the user in implementing the solution and solving the issues faced, which was presented to the system (102) in the form of challenge question.

[0070] In an implementation, the system (102) may be configured to use supervised, unsupervised or hybrid machine learning algorithm. In an embodiment, the input to the system (102) is the challenge question and its associated labels. The associated labels to the challenge question may include knowledge model or the overall operating plane (OOP) of the SMIS knowledge model mapped to the user, context derived for the challenge question, mapped insight and one or more practice models recommended by the system (102). The system (102) uses neural network having multiple layers and each layer contains a number of neurons. Each neuron receives an input which is the output of neurons from the previous layer. The inputs are added together and are further processed by a non-linear activation group. It is to be noted that implementation of machine learning algorithms through neural network is not restricted by the kinds of neural networks existing in the art.

[0071] Figure 3 illustrates an example flow chart of a method (300) for solving user-challenges presented in the form of challenge questions, in accordance with an example embodiment of the present disclosure. The method (300) may be depicted in the flow chart may be executed by a system, for example, the system (102) of Figure 1. In an example, the system (102) may be embodied in a computing device.

[0072] Operations of the flowchart and the combination of operation in the flowchart, may be implemented by several means including without limitation hardware, firmware, processor, circuitry and/or other devices associated with execution of software including one or more computer program instructions. For example, one or more of the procedures described in various embodiments may be embodied by computer program instructions. In an embodiment, the computer program instructions may be stored by at least one memory device of a system and executed by at least one processor in the system. Any such other computer program instructions may be loaded on a computer or other programmable system (for example, hardware) to produce a machine, such that the resulting computer or other programmable system embody means for implementing the operations specified in the flowchart. It will be noted herein that the operations of the method 300 are described with the help of the system (102). However, the operations of the method 300 can be described and/or practiced by using any other system.

[0073] At step 302, the method 300 includes authenticating the user to access the system (102), via one or more hardware processors. The step of user authentication includes receiving request from the user for accessing the system (102). The user request includes user credentials, for instance, name and password. The system (102) authenticates the user based on the received login credentials. The system (102) performs authentication by comparing the login credentials entered by the user with the login credentials pre-stored in the data store (222). If comparison indicates that the login credential entered into the system (102) by the user are correct, the user is authenticated and further access to the system (102) is allowed. On the other hand, if the comparison indicates that the login credentials provided by the user are incorrect, the user authentication fails and further access to the system (102) is restricted until the correct login credentials are entered by the user. Such authentication may be performed using authentication techniques known in the art, such as existing Lightweight Directory Access Protocol (LDAL) Directories.

[0074] At step 304, the method 300 includes selecting and assigning one of the operating planes of the SMIS knowledge model to the user, via one or more hardware processors. The step of selection and assignment of operating plane to the user includes selecting and assigning SMIS knowledge model to the user from the knowledge repository (112). The SMIS model is created basis the knowledge derived from the ancient texts, scriptures, their derivatives or a combination thereof, by the experts and practitioners in the field. The step of assigning the operating plane to the user includes utilizing one of the classifiers, quiz (es), survey instruments mapped to the SMIS knowledge model. The user is presented with one or more of the classifiers, quiz(es) or survey instruments and response of the user to said survey instruments, quiz(es) or classifiers is captured. The user response so captured is analyzed based on a scoring.

[0075] In an implementation, the survey instruments contain probes linked with sensors to capture the user response. The sensors may capture response of the user and each user response is assigned a score by the system (102). The analysis of the user response includes capturing score with each user response and calculating the complete score, basis which an overall operating plane (OOP) is mapped to the user. At this step, there is a possibility of identical scoring for two operating planes. At this step, the user is assigned the most appropriate OOP of the SMIS knowledge model through methodology, which is known in the art. In an example embodiment, the user with challenge question “Why am I not losing weight?” may be assigned “Senses” as the OOP of the SMIS knowledge model.

[0076] At step 306, the method 300 includes analyzing the challenge question posed by the user, via one or more hardware processors. The step of challenge question analysis includes receiving a challenge question from the user. The challenge question may be presented to the system (102) by the user in the form of sequence of text characters, sentences, paragraph, image, audible content and the like. In an example embodiment, the challenge question may be presented in the form of a sentence by the user i.e., “why am I not losing weight?”. At this step, the user is probed for additional information to gain required understanding of the input text provided in the form of challenge question by the user.

[0077] At step 308, the method 300 includes determining the user contextual details related to the challenge question, via one or more hardware processors. The step of determination of user-contextual details is facilitated by probing the user with instruments. In an embodiment, the instruments used for probing may be in the form of survey, visuals with necessary probes, sensory instruments. It is clarified that irrespective of the material nature of the instruments, the contextual data captured by said instruments is stored and analyzed, at this step via, one or more hardware processors. In an example embodiment, the user in response to challenge question “why am I not losing weight?” is probed with a quiz containing questions for capturing the context. In an embodiment, the quiz may be in presented in multiple-choice question format.

[0078] At step 310, the method 300 includes fetching one or more appropriate insights based on the mapped OOP and response of the user to the probing instruments, via one or more hardware processor. The step of fetching insights includes the system extracting one or more appropriate insight basis the identified OOP and the context derived from the user basis its response to the probing instruments. In an example embodiment, for the challenge question “Why am I not losing weight?”, an insight is extracted basis the OOP i.e., “Senses Plane” mapped at step 304 and the context derived from user response to quiz questions.

[0079] At step 312, the method 300 includes engineering the solution to the challenge question via, one or more hardware processors. The step of engineering the solution includes presenting the solution to the user in an understandable format basis the knowledge of sources other than ancient text. The solution presented to the user at this step includes the answer proposed for the challenge questions and recommendation of the system in terms of practices to be followed by the user to overcome the issues poses by the challenge question. In an embodiment, the engineered solution is presented in the same language as the language of the challenge question. In an example embodiment, for the challenge question “Why am I not losing weight”, the engineered solution includes the information about the OOP mapped to the user, one or more insight fetched for the challenge question and corresponding practice model extracted by the system (102) to the operating plane and insight extracted. For the challenge question “Why am I not losing weight”, the engineered solution may include the information about the OOP of the SMIS knowledge model (in present case, OOP means, “Senses” plane), insight extracted for the challenge question i.e., insight being “lack of interest by user”. The practice model corresponding to OOP as “Senses” plane and insight being “lack of interest of user” includes recommendations to follow the journey of similar users having faced the same issue and overcoming the issue, consult for help to overcome lack of interest and the like.

[0080] At step 314, the method 300 includes storing the solution proposed at step 310 in the system repository (110) for learning and creating a database for challenge questions and solutions to said challenge questions and understanding the user behavior. The creation of the database having the repository of challenge questions and related challenge questions will help in reduction of response time and better understanding of the context behind the challenge questions. Additionally, the creation of database for storing the solution to a challenge question will help the system (102) to compare the progress or evolution of the user in implementing the solution proposed to a particular challenge question and solving the issue faced. The system (102) may be used in a number of industry application including but not limited to counseling; employee performance and welfare; goal achievement; life transformational journey; effective diagnosis, treatment and care by healthcare professionals, improving smart learning systems, and the like. Examples of the industrial applications are not limited to the industry, sectors which have been mentioned hereinabove and the systems and methods described in the present disclosure may be applied to any situation where user feels the need to interact or interacts with a computational system having QA agents.

Example embodiment
[0081] Referring now to the example embodiment, an example scenario of the working of the system (102) is presented for determining context with the help of knowledge derived from Indian ancient text Bhagavad-Gita. Though the step of user authentication is depicted for the system (102) with reference to Figure 2 and Figure 3, such an authentication step need not be defined here.

[0082] Herein, at the beginning of the scenario, the challenge question is posed by a doctor to understand the issue of variation of patients’ response to identical or similar treatment plan having same physiological indicators. The challenge question entered into the system is based on the challenge faced by a medical practitioner while treating its patients depicting different response to same medical treatment having same physiological indicators. The challenge question posed to the system by the medical practitioner stated “How to treat patients effectively?”. For the purpose of this example embodiment, it is to be understood that the medical practitioners and the patients forming part of this embodiment are registered users on the system and have been authenticated by the system.

[0083] For understanding the context behind the challenge question and to provide a comprehensive solution, the survey instruments having multiple sensors were deployed. In an implementation, the system may deploy a question answering agent probing patients for additional response by providing identical set of multiple-choice questions to the patients and collecting their response to said question set. The survey instrument consisted of 8 probes, with 2 sensors each and. Thus the 8 probes, involving a total of 16 sensors, were applied to each subject. The subject being the patients treated by the medical practitioner.

[0084] The sensors were in the form of a text string in English language. All the sensors were mapped to one of the four operating planes of SMIS-Model. The subjects responded to a probe by selecting one of the sensors in the probe. Response to a probe thus returned a frequency count of the operating plane corresponding to the sensor selected by the subject. A subject’s response to the instrument thus comprised frequency counts for the four operating planes of SMIS-Model. The frequency count of one of the operating planes is thus incremented by one for the subject’s response to each probe. At the end of the 8th probe, the operating plane with the maximum frequency count was considered as the OOP, which gave SMIS-Category of the subject.

[0085] The variables S, M, I, and S represent frequency counts of Senses (Body), Mind, Intelligence, and Soul operating planes respectively, in a subject’s response to the instrument. These counts get updated every time the subject responds to a probe. According to the hierarchy of the operating planes, as defined in the SMIS-Model, the Mind is above the Senses, Intelligence is above the Mind, and Soul is above the Intelligence. This hierarchy is used as a tie-breaker, by selecting the highest operating plane from amongst those which occurred with equal frequency, while determining the OOP.

[0086] In the example scenario, based on the response of the subjects to the survey probes, the system (102) assigned one OOP to each subject and provided the details of the operating planes assigned to each subject to the medical practitioner posing the challenge question. The system may also assign an OOP to the medical practitioner in an example embodiment. Basis the response of the subjects to the probes, the system fetches appropriate insights for each of the subjects and provides the complete data set of the operating plane and related insights for each subject to the medical practitioner.

[0087] In an example scenario, out of 30 subjects, 8 were mapped to the senses plane, 16 were mapped to the mind plane, 4 were mapped to the intelligence plane and rest 2 were mapped to the soul plane. The mapping of subjects to different planes by the system (102) suggests that the subjects that are at Senses plane lacks the ability to provide complete medical history to the medical practitioner, subjects at the Mind plane have anxiety causing trust issues leading to failure in following advice of the medical practitioner, while the subjects at Intelligence plane have issues with following doctor’s instructions without understanding the treatment plan and subjects at Soul plane tend to be less attentive to the medical concerns. In addition, the system (102) provides the medical practitioner with or answers (practices) in a user understandable language format with regards to the practices to be followed to mitigate the challenges.

[0088] The system (102) to the challenge question “How to treat patients effectively” provides an engineered solution as to why there is difference in the treatment response between the subjects and how to improve or reduce the variations in the response by implementing the practices or engineered solution suggested by the system.

[0089] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device 20 may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.

[0090] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0091] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

[0092] Various figures of the present application include block diagrams, flowcharts and control flow illustrations of methods, systems and program products according to the invention, it will be understood that each block or step of the block diagram, flowchart and control flow illustration, and combinations of blocks in the block diagram, flowchart and control flow illustration, can be implemented by computer program instructions. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the block diagram, flowchart or control flow block(s) or step(s). These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the block diagram, flowchart or control flow block(s) or step(s). The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the block diagram, flowchart or control flow block(s) or step(s).

[0093] Accordingly, blocks or steps of the block diagram, flow chart or control flow illustration support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block or step of the block diagram, flowchart or control flow illustration, and combinations of blocks or steps in the block diagram, flowchart or control flow illustration, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.

[0094] The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.

[0095] Some embodiments, illustrating its features, will now be discussed in detail. The words "comprising", "having", "containing" and "including" and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a" "an" and "the" include plural references unless the context clearly dictates otherwise. Although any systems, methods, apparatuses, and devices similar or equivalent to those described herein can be used in the practice or testing of embodiments, the preferred methods and systems now described. The disclosed embodiments are merely exemplary.

[0096] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

,CLAIMS:WE CLAIM:

1. A processor-implemented method for solving user-centric challenges using ancient text intelligence ontology, the method comprising:
a) authenticating a user basis login credentials of the user;
b) selecting and assigning a knowledge model pre-stored in a system repository to the user, wherein the knowledge model is derived from one or more ancient texts and scriptures;
c) analyzing a challenge question related to the user and obtaining context from the user related to the challenge question;
d) fetching one or more insights basis the analysis of challenge question, context obtained from the user and the knowledge model mapped to the user, wherein one or more insights are extracted from the system repository containing a plethora of insights derived from one or more ancient texts and scriptures; and
e) providing an engineered solution to the user containing one or more insights and one or more practice models in a format understandable to the user, wherein one or more practice models are derived from one or more ancient texts and scriptures and are stored in the system repository.

2. The processor implemented method as claimed in claim 1, wherein selecting and assigning of the knowledge model to the user further comprising assigning of SMIS (Senses, Mind, Intelligence and Soul) knowledge model to the user.

3. The processor-implemented method as claimed in claim 2, wherein selecting and assigning of SMIS knowledge model to the user further comprising mapping an operating plane of the SMIS knowledge model to the user, wherein the SMIS knowledge model consists of Senses, Mind, Intelligence and Soul operating planes in hierarchy with Senses plane at lowermost position and Soul plane at highest position.

4. The processor-implemented method as claimed in claim 3, wherein the assignment of an operating plane of the SMIS knowledge model to the user to determine the overall operating plane of the user, is based on existing user information available in the system repository and response of the user to one or more survey instruments.

5. The processor-implemented method as claimed in claim 1, wherein the analysis of challenge question depends on variables including challenge question type, contents of challenge question and length of challenge question.

6. The processor-implemented method as claimed in claim 1, wherein obtaining user context from the user further comprising probing the user for contextual information with sensory instruments.

7. The processor-implemented method as claimed in claim 6, wherein the sensory instruments include at least one of quiz, visuals with necessary probes and surveys.

8. The processor-implemented method as claimed in claim 1, wherein one or more practice models derived from ancient text and scripture, are in the form of recommendations to be followed by user for overcome one or more issues contained in the challenge question.

9. The processor-implemented method as claimed in claim 1, wherein the ancient text and scriptures include at least one of ancient Indian scriptures including Vedas, Puranas, Upanishads, Bhagavad Gita and international ancient scriptures including Bible, Quran, Book of Mormon, Torah or a combination thereof.

10. The processor-implemented method as claimed in claim 1, further comprising storing the engineered solution for each challenge question presented by the user to build solution dataset in the system repository and keep track of user progress.

11. The processor-implemented method as claimed in claim 1, further comprising retraining and improving one or more knowledge models and one or more practice models using machine learning.

12. A system for solving user-centric challenges using ancient scripture intelligence ontology, the system comprising:
a memory storing instructions;
an input/output interface; and
one or more hardware processor coupled to the memory via the input/output interface, wherein the one or more hardware processors are configured by instructions to:
authenticate a user basis login credentials of the user;
assign a knowledge model pre-stored in a system repository to the user, wherein the knowledge model is derived from one or more ancient scriptures;
analyze a challenge question related to the user and obtaining context from the user related to the challenge question;
fetch fetching one or more insights basis the analysis of challenge question, context obtained from the user and the knowledge model mapped to the user, wherein one or more insights are extracted from the system repository containing a plethora of insights derived from one or more ancient scriptures; and
provide an engineered solution to the user containing one or more insights and one or more practice models in a format understandable to the user, wherein one or more practice models are derived from one or more ancient scriptures and are stored in the system repository.

13. The system of claim 11, wherein selecting and assigning of the knowledge model to the user further comprising assigning of SMIS (Senses, Mind, Intelligence and Soul) knowledge model to the user.

14. The system of claim 12, wherein selecting and assigning of SMIS knowledge model to the user further comprising mapping an operating plane of the SMIS knowledge model to the user, wherein the SMIS knowledge model consists of Senses, Mind, Intelligence and Soul operating planes in hierarchy with Senses plane at lowermost position and Soul plane at highest position.

15. The system of claim 13, wherein the assignment of an operating plane of the SMIS knowledge model to the user to determine the overall operating plane of the user, is based on existing user information available in the system repository and response of the user to one or more survey instruments.

16. The system of claim 11, wherein the analysis of challenge question depends on variables including challenge question type, contents of challenge question and length of challenge question.

17. The system of claim 11, wherein obtaining user context from the user further comprising probing the user for contextual information with sensory instruments.

18. The system of claim 16, wherein the sensory instruments include at least one of quiz, visuals with necessary probes and surveys.

19. The system of claim 11, wherein one or more practice models derived from ancient scripture are in the form of recommendations to be followed by user for overcome one or more issues contained in the challenge question.

20. The system of claim 11, wherein the ancient scripture includes at least one of ancient Indian scriptures including Vedas, Puranas, Upanishads, Bhagavad Gita and international ancient scriptures including Bible, Quran, Book of Mormon, Torah or a combination thereof.

21. The system of claim 11, further comprising storing the engineered solution for each challenge question presented by the user to build solution dataset in the system repository and keep track of user progress.

22. The system of claim 11, further comprising retraining and improving one or more knowledge models and one or more practice models using machine learning.

23. A non-transitory computer readable medium storing one or more instructions which when executed by one or more processors on a system, cause the one or more processors to perform method comprising:
a) authenticating a user basis login credentials of the user;
b) selecting and assigning a knowledge model pre-stored in a system repository to the user, wherein the knowledge model is derived from one or more ancient texts and scriptures;
c) analyzing a challenge question related to the user and obtaining context from the user related to the challenge question;
d) fetching one or more insights basis the analysis of challenge question, context obtained from the user and the knowledge model mapped to the user, wherein one or more insights are extracted from the system repository containing a plethora of insights derived from one or more ancient texts and scriptures; and
e) providing an engineered solution to the user containing one or more insights and one or more practice models in a format understandable to the user, wherein one or more practice models are derived from one or more ancient texts and scriptures and are stored in the system repository.