FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION (See Section 10 and Rule 13)
Title of invention:
TEMPORAL REPRESENTATION OF EMERGING TECHNOLOGIES IN
A TABLE DEPICTING TECHNOLOGY CONVERGENCE WITH
INTERACTIVE INFERENCING
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD [001] The disclosure herein generally relates to the field of data mining and generating consumable inferences, and, more particularly, to systems and methods for temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing.
BACKGROUND
[002] Emerging technologies are catalysts for change, offering extraordinary new capabilities to innovation. Adoption of these technologies allows forward-thinking and reimagining the possible. There are a number of sources that periodically publish a list of emerging technologies they consider will make the most impact in the next several years. These sources are either analysts (Gartner, McKinsey, etc.) or Futurologists (Future Today Institute, Imperial Tech Foresight, etc.). These sources do not apply the same criteria or look at all areas of technology with equal emphasis. As a result, there are several divergent versions of what future technology trends might look like. The methodology relies significantly on in-house expert opinions and are not disclosed for independent verification. There is no benchmark by which the various sources can be evaluated.
[003] To enable insights and foresights, a formal and standardized approach to mine, analyze and derive inferences is required. Since there are multiple sources of information with varying degree of forecasting accuracy, and since different sources often concentrate on a limited category of technologies, it is challenging to correlate data from diverse sources, generate a list of emerging technologies (including in-house sources) and objectively evaluate them based on meaningful criteria to derive inferences on their putative transformative capability.
[004] Input data from varying sources are quite large, making traditional visualization or inferencing techniques impractical. Inferencing from large volumes of data is tedious as well as subjective, with very little scope of flexibility in analysis considering the dependency on domain experts. State-of-the-art techniques rely on examining patent citation networks and link prediction techniques that may not provide reliable output at the early stages of evolution of the emerging technologies.

SUMMARY
[005] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[006] In an aspect, there is provided a processor implemented method comprising the steps of: obtaining, via one or more hardware processors, a plurality of artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from a technology corpus of interest to an enterprise, wherein the one or more technologies of interest are one or more emerging technologies for a current time period; predicting, via the one or more hardware processors, a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries; measuring, via the one or more hardware processors, a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts; and auto-suggesting, via the one or more hardware processors, a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more characteristics of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table.
[007] In another aspect, there is provided a system comprising: memory storing instructions; one or more communication interfaces; one or more hardware

processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: obtain a plurality of artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from a technology corpus of interest to an enterprise, wherein the one or more technologies of interest are one or more emerging technologies for a current time period; predict a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries; measure a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts; and auto-suggest a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more characteristics of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table.
[008] In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain a plurality of artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from a technology corpus of interest to an enterprise,

wherein the one or more technologies of interest are one or more emerging technologies for a current time period; predict a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries; measure a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts; and auto-suggest a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more characteristics of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table.
[009] In accordance with an embodiment of the present disclosure, the one or more hardware processors are configured to predict a time horizon for each of the one or more emerging technologies by: classifying the one or more emerging technologies into an associated stage of innovation based on the obtained artifacts, wherein the associated stage of innovation is one of (i) basic research evidenced by the obtained abstracts of research papers, (ii) applied research evidenced by the obtained abstracts of research papers and the patent documents, and (iii) technology development evidenced by startup launch and new product, service, Line Of Business announcements; plotting the obtained artifacts associated with each of the one or more emerging technologies classified under each stage of innovation over a predefined timeline; identifying on the predefined timeline, a level of activity associated with each of the one or more emerging technologies classified under

each stage of innovation, based on volume, citation or funds associated with each of the one or more emerging technologies; computing, the CoT based on the level of activity associated with each of the one or more emerging technologies, using a Bass Diffusion model, the CoT being an indicator for the slope of a Bass curve and is computed as a weighted aggregation of the of the yearly total activity levels for the predefined timeline; the TDL is based on number of startups identified, from the obtained artifacts, having a Series A funding greater than a predetermined amount and is indicative of a position of each of the one or more emerging technologies on the Bass curve; and the TVL is based on a percentage of startups which received Series B funding identified from the obtained artifacts; and predicting the time horizon for each of the one or more emerging technologies based on a mapping of the overall technology score with a time horizon on the predefined timeline.
[010] In accordance with an embodiment of the present disclosure, the one or more hardware processors are configured to to measure a future impact of each of the one or more emerging technologies on end users by: extracting entities comprising (i) the one or more emerging technologies, (ii) the industries, and (iii) uses cases from the obtained artifacts using a pretrained Named Entity Recognition (NER) model; performing the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries, wherein a first level mapping is performed between the extracted use cases and one or more emerging technologies using an Natural Language Processing (NLP) parser to resolve semantic relationships between the extracted entities; and a second level mapping is performed between each of the extracted use cases previously mapped to the one or more emerging technologies and one or more of the industries, based on one or more of (i) identified keywords for each of the industries, (ii) industry core and ancillary Jobs to be Done (JTBD) identified from a taxonomy of industries, and (iii) industry digital Jobs to be Done (D-JTBD) identified from reports on industry-wise digitization scope; and measuring the future impact of each of the one or more emerging technologies on the end users using a look up table on the mapped extracted use cases, the one or more emerging technologies and the industries,

wherein the look up table is generated based on parameters including (i) a Customer Proximity Score (CPS) indicative of a reach of each of the extracted use cases and thereby an associated technology (ii) an Industry Impact Score (IIS) indicative of the impact of each of the extracted use cases and thereby an associated technology based on fulfilment of core, ancillary or peripheral job in an associated industry, (iii) an Industry Adoptability Score (IAS) indicative of adoption within the industry of each of the extracted use cases and thereby an associated technology, and (iv) Cross-Industry Adaptability Score (CAS) indicative of applicability of each of the extracted use cases and thereby an associated technology to a plurality of industries.
[011] In accordance with an embodiment of the present disclosure, the identified keywords pertaining to each of the industries are obtained using a network analysis comprising: extracting descriptive content for each of the industries from the obtained artifacts; extracting potential keywords related to each of the industries using a Term frequency-inverse document frequency (TF-IDF) measure; generating a network with the extracted potential keywords as nodes and co-occurrence of the extracted keywords in a sentence as an edge; and identifying a predefined number of keywords from the extracted potential keywords, for each of the industries using a Weighted Degree Centrality (WDC) associated with the generated graph.
[012] In accordance with an embodiment of the present disclosure, (i) the CPS is computed using an ecommerce function-based classification of each of the industries, (ii) the IIS score is based on the identified JTBD (iii) the IAS is based on the computed CoT, and (iv) the CAS is based on the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries.
[013] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to refine the measured future impact of each of the one or more emerging technologies using a Q-learning method with a current state of each of the emerging technologies being characterized by 4-tuple of scores including the CPS, the IIS, the IAS and the CAS.
[014] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to determine, an alignment of

each of the one or more emerging technologies with a set of trends associated with the one or more emerging technologies, using (i) a paragraph vector of each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend from the set of trends and (ii) a sentiment analysis; prior to auto-suggesting a position for each of the one or more emerging technologies in a cell.
[015] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to determine an alignment of each of the one or more emerging technologies with a set of trends by: obtaining (i) the set of trends, (ii) related content thereof and (iii) the list of the one or more emerging technologies and the associated artifacts, from the technology corpus of interest to the enterprise; computing a sentiment score for each sentence associated with each trend from the set of trends, using the sentiment analysis on the related content; computing a paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend using a pretrained Document Vector library; computing one or more of (i) a first alignment score representing the alignment of each of the one or more emerging technologies with each trend using the computed paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend, and (ii) a second alignment score representing the alignment of a technology group cluster comprising the one or more emerging technologies with each trend, wherein the second alignment score is obtained by summing, the first alignment score for each of the one or more emerging technologies, in the technology group cluster, with each trend; and assigning an alignment rank to each technology group cluster corresponding to an associated overall technology group alignment score based on the computed second alignment score for each trend.
[016] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to compute the first alignment score by performing, for each of the one or more emerging technologies with each trend, the steps of: computing a similarity score between (i) an emerging technology

from the one or more emerging technologies and (ii) each sentence associated with a trend; obtaining a product of (i) the similarity score of each sentence associated with a trend and (ii) the computed sentiment score, only for the sentences having a similarity score greater than 90%; and obtaining the first alignment score representing the alignment of the emerging technology with the trend, as a summation of the obtained products for the sentences having the similarity score greater than 90%.
[017] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to receive, from the technology corpus of interest to an enterprise, one or more characteristics of the one or more emerging technologies including (i) a technology group cluster, for the current time period, to which each of the one or more emerging technologies belongs, (ii) a technology family from a group of technology families, to which each technology group cluster belongs, (iii) a computed reactivity of each technology group cluster with other technology group clusters, (iv) an importance rank associated with each of the one or more emerging technologies in a list of emerging technologies; and further characteristics of the one or more emerging technologies including one or more of (v) the predicted time horizon for each of the one or more emerging technologies, (vi) a measured future impact of each of the emerging technologies, and (vii) the alignment rank associated with each of the emerging technologies; prior to auto-suggesting, a position for each of the one or more emerging technologies in a cell amongst a plurality of cells.
[018] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to provide an interactive data visualization associated with one or more cells in the plurality of cells, wherein the data visualization provides information pertaining to one or more of (i) the one or more characteristics and (ii) inferences pertaining to the extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts.

[019] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to refine the position for each of the one or more emerging technologies in a cell, using a Q-learning method.
[020] In accordance with an embodiment of the present disclosure, the one or more hardware processors are further configured to input the generated table into at least one of (i) a business transformation tool to generate transformation steps to be executed and (ii) an enterprise-wide search engine for identifying solutions using emerging technologies.
[021] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[022] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[023] FIG.1 illustrates an exemplary block diagram of a system for temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing, in accordance with some embodiments of the present disclosure.
[024] FIG.2A through 2B illustrates an exemplary flow diagram of a computer implemented method for temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing, in accordance with some embodiments of the present disclosure.
[025] FIG.3 illustrates classification of obtained artifacts under different stages of innovation over an exemplary timeline, in accordance with some embodiments of the present disclosure.
[026] FIG.4 illustrates an exemplary temporal representation of emerging technologies in a table, in accordance with some embodiments of the present disclosure.

[027] FIG.5A through FIG.5D illustrate various exemplary visual indicators that facilitate inferences via the table of FIG.4, in accordance with some embodiments of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS [028] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[029] Since emerging technologies are in a nascent stage, not much published information is available. If early convergences between the emerging technologies are detected, there is a lot of potential to develop new capabilities in a timely manner. Considering the diverse sources and formats that may exist for information regarding the emerging technologies, it is challenging to mine specific information, analyze and derive inferences in an objective manner. As the volume of data increases, so does the complexity of the visual representations. The visual representations either end up crammed with information or do not enable meaningful inferencing. To understand convergence between emerging technologies, it is important to understand the position of each emerging technology with respect to the other emerging technologies and various characteristics associated with each of the emerging technologies over a period of time. It is important to keep track of data, where it is sourced from, the transformations that have been performed on the data and the outcome for each time period to enable any kind of prediction regarding the emerging technologies. The present disclosure provides a temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing which addresses the above-mentioned challenges and requirements.

[030] In the context of the present disclosure, the expression ‘emerging technologies’ are technologies associated with unrealized development, practical applications, or both.
[031] In the context of the present disclosure, the expressions ‘technology terms’ and ‘technology’ may be used interchangeably when referring to ‘emerging technologies’.
[032] In the context of the present disclosure, ‘diffusion state’ of a technology refers to the usage or impact of the technology across industries or domains.
[033] In the context of the present disclosure, ‘unitary technologies’ refers to core technologies or digital technologies such as Artificial Intelligence (AI) and NextGen Computing, that are derivatives of Computer Sciences and Information Technology. These are foundational blocks of digital systems and include software, hardware, communications (network) and data systems and can potentially deliver exponential impact on applied business. Certain technologies, such as space, whose impact is yet to be observed but cannot be ignored because these could potentially alter existing commerce paradigms are also considered as unitary technologies.
[034] In the context of the present disclosure, ‘technology domains’ refers to digital technologies such as Data & Intelligence, and Material Science, that can create exponential impact across social, political and economic world. These technologies usually build over multiple core technologies and have cross-industry relevance. They explicate combinatorial possibilities at the confluence of the physical, digital and biological worlds and lead the change towards more connected and inter-dependent commerce. Digital platforms are also included under technology domains.
[035] In the context of the present disclosure, ‘industry domains’ refers to industry imperatives derived from human needs, further equated with the forces of disruption (e.g. infrastructure, demographics, etc.) and emerging ecosystems (e.g. Mumbai, Jakarta, etc.). Industry imperatives are expected to have longevity and will mostly remain invariant over many years. Some examples include Drug Discovery & Development and Genetic Engineering.

[036] Applicants’ Indian Patent Application No. 202121036165 titled ‘Automatic Identification, Ranking And Grouping Of Emerging Technologies From Diverse Sources’ provides a system and method to generate a ranked list of emerging technologies from diverse and subjective sources and grouping them into technology families in a systematic and objective manner, the rank (importance rank) being associated with an importance or authority measure. The system and method systematically identify a ranked list of emerging technologies from diverse sources; eliminate duplicate technology terms; and performs multi-stage clustering of de-duplicated emerging technologies to obtain a set of technology clusters that can be mapped to a plurality of technology families such as the unitary technologies, technology domains and industry domains. Having grouped the de-duplicated emerging technologies into technology clusters and further classified under the technology families, using a network of nodes representing technology groups and edges characterized by common keywords across the nodes, collaborative capabilities (reactivity) of the technology groups are assessed and the technologies are suitably ranked. A technology corpus of interest to an enterprise was also provided that was periodically updated with at least the ranked list of emerging technologies, associated technology terms, description and keywords.
[037] The system and method of the present disclosure also utilizes the technology corpus of interest to the enterprise provided by the Applicant in the Indian Patent Application No. 202121036165. However, it may be understood by those skilled in the art that the contents of the technology corpus of interest to the enterprise may be obtained by methods provided in the Applicants’ Patent Application No. 202121036165 or by any other methods known in the art. The system of the present disclosure periodically crawls a plurality of sources such as (i) internet sources including one or more web pages pertaining to the emerging technologies and (ii) databases to obtain artifacts (characterized later in the description) pertaining to the emerging technologies for data mining and updating the technology corpus of interest to the enterprise. The plurality of sources may include in-house sources as well. The system of the present disclosure may also use

Named entity Recognition extraction and Regular Expression(regex) patterns to mine data pertaining to the emerging technologies from offline documents.
[038] Referring now to the drawings, and more particularly to FIG. 1 through FIG.5D, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[039] FIG.1 illustrates an exemplary block diagram of a system 100 for temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing, in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface (s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more hardware processors 104 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In the context of the present disclosure, the expressions ‘processors’ and ‘hardware processors’ may be used interchangeably. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
[040] The communication interface (s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface(s) can include one or more ports for connecting a number of devices to one another or to another server.

[041] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory 102.
[042] FIG.2A through FIG.2B illustrates an exemplary flow diagram of a computer implemented method 200 for temporal representation of emerging technologies in a table depicting technology convergence with interactive inferencing, in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 includes the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions configured for execution of steps of the method 200 by the one or more hardware processors 104. The steps of the method 200 will now be explained in detail with reference to the components of the system 100 of FIG.1. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[043] In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to obtain, at step 202, a plurality of the artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from the technology corpus of interest to the enterprise, wherein the one or more technologies of interest are one or more emerging technologies for a current time period.
[044] Estimating technology maturation for emerging technologies is challenging because of lack of adequate data for performing a reasonably accurate

analysis. State-of-the-art forecasting methods suffer from lack of historic data of the appropriate kind for the enterprise that needs a time to mature assessment of a technology. In accordance with the present disclosure, complexity of the technology, time needed for development and time needed for validation are used to predict a time horizon for each of the one or more emerging technologies.
[045] Accordingly, in an embodiment of the present disclosure, the one or more hardware processors 104, are configured to predict, at step 204, a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of position of each of the one or more emerging technologies on a Bass curve and time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries.
[046] In an embodiment, the step of predicting a time horizon for each of the one or more emerging technologies comprises classifying the one or more emerging technologies into an associated stage of innovation based on the obtained artifacts. In an embodiment stages of innovation may be identified using definitions provided by the National Science Foundation (NSF). Accordingly, the associated stage of innovation may be one of (i) basic research evidenced by the obtained abstracts of research papers, (ii) applied research evidenced by the obtained abstracts of research papers and the patent documents, and (iii) technology development evidenced by startup launch and new product, service, Line Of Business announcements. In an embodiment, the stage of innovation may be based on Gartner’s Hype cycle. Supervised learning methods may be employed for the step of classifying.
[047] As mentioned above, the system 100 periodically crawls the plurality of sources and stores artifacts in the technology corpus of interest to the enterprise. The artifacts related to the basic research may be sourced from publications by Elsevier, The Institute of Electrical and Electronics Engineers (IEEE), The Association for Computing Machinery (ACM), and the like. The

artifacts related to patent documents may be sourced from patent office websites like https://ipindia.gov.in/index.htm (Indian patent office), https://www.uspto.gov/ (United States Patent Office), and the like. The artifacts related to technology development may be sourced from Management Information Systems (MIS), Forbes, CrunchBase, and the like.
[048] As part of the step of predicting the time horizon for each of the one or more emerging technologies, the obtained artifacts associated with each of the one or more emerging technologies classified are plotted under each stage of innovation over a predefined timeline. The predefined timeline is empirically determined. In an embodiment the predefined timeline is 6 years since a lot of traction in emerging technologies is typically noted in this time period and it is expected that at least two stages may be observed for a technology in this time period.
[049] As part of the step of predicting a time horizon for each of the one or more emerging technologies, a level of activity associated with each of the one or more emerging technologies classified under each stage of innovation, based on volume, citation or funds associated with each of the one or more emerging technologies, is identified on the predefined timeline. FIG.3 illustrates classification of the obtained artifacts under different stages of innovation over an exemplary timeline, in accordance with some embodiments of the present disclosure. To develop the timeline for some exemplary emerging technologies, artifacts related to the three stages of innovation are obtained for an exemplary time period 2014 – 2021 from the technology corpus of interest to the enterprise. The level of activity associated with each of the emerging technologies is depicted by circles of varying sizes for low activity and high activity respectively, based on an empirically determined threshold.
[050] As part of the step of predicting a time horizon for each of the one or more emerging technologies, the CoT is computed using a Bass Diffusion model, based on the level of activity associated with each of the one or more emerging technologies, the CoT being an indicator for the slope of a Bass curve. In accordance with the present disclosure, the CoT is an indirect measure of

complexity, wherein higher the cost, higher is the time needed for adoption by industries.
[051] Table 1 below provides possible level of activities associated with each of the one or more emerging technologies classified under each stage of innovation. Table 1:

Activity 5 years 4 years 3 years 2 years 1 year Current
ago ago ago ago ago year
Basic High/Low/ High/Low/ High/Low/ High/Low/ High/Low/ High/Low/
research Nil Nil Nil Nil Nil Nil
Applied High/Low/ High/Low/ High/Low/ High/Low/ High/Low/ High/Low/
research Nil Nil Nil Nil Nil Nil
Tech. High/Low/ High/Low/ High/Low/ High/Low/ High/Low/ High/Low/
Dev. Nil Nil Nil Nil Nil Nil
Yearly A1 A2 A3 A4 A5 A6
total
activity
level
[052] In an embodiment, the CoT is computed as a weighted aggregation of the yearly total activity levels for the predefined timeline. For instance, in an embodiment, the CoT = 1/3 * (A1 + A2 + A3) + 2/3 * (A4 + A5 + A6), assigning higher weight to scores in later years. The computation of the CoT is explained via an example provided below.
[053] Table 2 illustrates yearly computed CoT by assigning points as shown, wherein the high activity level is assigned a score of 2, the low activity level is assigned a score 1 and Nil is assigned a score 0. Table 2:

Technology 2014 2015 2016 2017 2018 2019 Cost
Distributed Ledger Basic Research 0 0 0 0 1 1

Applied Research 0 0 0 1 1 1
Technology Development 1 1 1 2 2 2
Yearly CoT 1 1 1 3 4 4 8.33
API Economy Basic Research 0 0 0 0 0 0
Applied Research 0 0 1 1 1 1
Technology Development 0 1 0 0 0 0
Yearly CoT 0 1 1 1 1 1 2.67
3d technology Basic Research 1 1 1 1 0 0
Applied Research 1 1 1 1 1 1
Technology Development 2 2 2 2 2 2
Yearly CoT 4 4 4 4 3 3 10.67
Decentralized Web Basic Research 0 0 0 0 0 0
Applied Research 1 1 1 1 1 1
Technology Development 0 0 0 1 1 1
Yearly CoT 1 1 1 2 2 2 5
iPaaS Basic Research 0 0 0 0 0 0
Applied Research 1 1 0 0 0 0
Technology Development 1 1 1 1 1 1
Yearly CoT 2 2 1 1 1 1 3.67
Exoskeleton Basic Research 1 1 1 1 2 2
Applied Research 1 1 1 1 1 2
Technology Development 1 1 1 1 1 1
Yearly CoT 3 3 3 3 4 5 11

Cognitive Computing Basic Research 0 2 2 2 1 1
Applied Research 1 2 1 1 1 1
Technology Development 1 1 1 1 1 1
Yearly CoT 2 5 4 4 3 3 10.33
Ensemble Learning Basic Research 1 1 1 1 2 2
Applied Research 1 1 1 2 2 2
Technology Development 1 1 1 1 0 0
Yearly CoT 3 3 3 4 4 4 11
Carbon Nanotube Basic Research 1 1 2 2 1 1
Applied Research 1 1 1 2 1 1
Technology Development 1 1 1 1 1 0
Yearly CoT 3 3 4 5 3 2 10
Virtualization Basic Research 2 2 2 2 2 2
Applied Research 2 2 2 2 2 2
Technology Development 2 2 2 2 2 2
Yearly CoT 6 6 6 6 6 6 18
[054] In an embodiment, the TDL is based on number of startups identified, from the obtained artifacts, having a Series A funding greater than a predetermined amount. For instance, a startup database like CrunchBase maybe used to scan startups in an emerging technology. Startups receive a seed funding when they start operations, typically between 100K USD and 5 Million USD. As per 2020 statistics, a startup may need 2 years to get Series A funding and a further 2 years to obtain Series B funding. Accordingly, in an embodiment, the predetermined amount is empirically determined to be 5 Million USD. In accordance with the present disclosure, the TDL is indicative of a position on the Bass curve (how far a technology has traveled on the Bass curve) and hence how

much more time is needed for development. Table 3 below indicates exemplary scores provided for the time taken to get Series A funding. Table 3:

One or more 4 to 6 years ago 2 to 4 years ago Now to 2 years
startups spotted ago
with cumulative
funding > 5
Million USD
Score 1 2 3
[055] Table 4 below provides a score representing the TDL for an exemplary emerging technology. Table 4:

Emerging technology Timeline of startup
found with > 5 million
USD funding Score based on Table 3
Distributed Ledger 4 to 6 years ago 1
[056] In an embodiment, the TVL is based on a percentage of startups which received Series B funding identified from the obtained artifacts. Table 5 below indicates exemplary scores corresponding to the percentage of startups that received Series B funding. Table 5:

Percentage of 0 to 5% 5 to 25% Greater than
startups that 25%
received Series B
funding for an
emerging
technology
Score 1 2 3
[057] In accordance with the present disclosure, the TVL measures validation of the technology by the industries and helps in proper categorization of

these long incubation technologies. Since startups receive Series B funding only when they have successfully validated the technology model by reaching out to early adapters, in accordance with the current disclosure, the TVL is deemed a validation milestone for the technology.
[058] Table 6 below indicates scores corresponding to the percentage of startups that received Series B funding corresponding to some exemplary emerging technologies. Table 6:

Emerging No. of No. of Series % Startup Score
Technologies startups B funding having series B based
startups funding on
Table
5
Distributed 131 5 3.8167938931 1
Ledger
API Economy 0 0 0 1
3d technology 757 54 7.1334214003 2
Decentralized 6 0 0 1
Web
iPaaS 18 1 5.5555555556 2
Exoskeleton 21 4 19.0476190476 2
Cognitive 43 5 11.6279069767 2
Computing
Ensemble 5 0 0 2
Learning
Carbon Nanotube 5 1 20 2
Virtualization 255 49 19.2156862745 2
[059] In an embodiment of the present disclosure, the time horizon for each of the one or more emerging technologies is predicted based on a mapping of the overall technology score with a time horizon on the predefined timeline. In an embodiment, the overall technology score is computed as COT * TDL * TVL.

Table 7 below indicates an exemplary mapping of the overall technology score with associated predicted time horizons. Table 7:

Overall technology score Predicted time horizon
1 H0
2 H0
3 H1
4 H1
6 H2
8 H2
9 H3
12 H3
18 H3
27 H3
[060] Table 8 below indicates a mapping of the overall technology score with associated predicted time horizons for some exemplary emerging technologies. Table 8:

Technology CoT TDL TVL Overall
technology
score Prediction Time horizon
Distributed Ledger 8 1 1 8 H2
API Economy 3 1 1 3 H1
3d technology 11 1 2 22 H3
Decentralized Web 5 2 1 10 H3
iPaaS 4 1 2 8 H2
Exoskeleton 11 1 2 22 H3
Cognitive Computing 10 1 2 20 H3

Ensemble Learning 11 1 2 22 H3
Carbon Nanotube 10 1 2 20 H3
Virtualization 18 1 2 36 H3
[061] Impact analysis methods known in the art focus on identifying a broad range of impacts such as social, economic and market growth already seen or that has occurred. In accordance with the present disclosure, the method 200 addresses gauging or predicting the disruptive behavior of the technology when it enters the market, particularly quantifying a future impact on number of end users. In accordance with the present disclosure, use cases for the technology in an industry are extracted, characterized in terms of key industry jobs to be done and aggregated across industries to assess the impact.
[062] Accordingly, in an embodiment of the present disclosure, the one or more hardware processors 104, are configured to measure, at step 206, a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts.
[063] In accordance with the present disclosure, entities including (i) the one or more emerging technologies, (ii) the industries, and (iii) uses cases are extracted from the obtained artifacts using a pretrained Named Entity Recognition (NER) model. The NER is typically a pretrained statistical model such as a Conditional random field (CRF) model. In an embodiment, the one or more emerging technologies may be extracted by the method described by the Applicant in the Indian Patent Application No. 202121036165.
[064] In accordance with the present disclosure, as part of the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries, a first level mapping is performed between the extracted use cases and one or more emerging technologies using an NLP parser to resolve semantic relationships between the extracted entities. A second level mapping is then performed between each of the extracted use cases previously mapped to the

one or more emerging technologies and one or more of the industries (some functions like digital marketing are applicable across industry domains), based on one or more of (i) identified keywords for each of the industries, (ii) industry core and ancillary Jobs to be Done (JTBD) identified from a taxonomy of industries [using artifacts such as Global Industry Classification Standard (GICS)], and (iii) industry digital Jobs to be Done (D-JTBD) identified from reports [using artifacts such as World Economic Forum (WEF)] on industry-wise digitization scope.
[065] In an embodiment, the identified keywords pertaining to each of the industries are obtained using a network analysis, wherein descriptive content are extracted for each of the industries from the obtained artifacts. Potential keywords related to each of the industries are then extracted using a Term frequency-inverse document frequency (TF-IDF) measure. A network is generated using the extracted potential keywords as nodes and co-occurrence of the extracted keywords in a sentence as an edge. A predefined number (say 20) of keywords are identified from the extracted potential keywords, for each of the industries using a Weighted Degree Centrality (WDC) associated with the generated graph.
[066] The mapping of the use cases to one or more emerging technologies and one or more industries is done using word2vec models. The future impact of each of the one or more emerging technologies on end users is measured using a look up table on the mapped extracted use cases, the one or more emerging technologies and the industries. In accordance with the present disclosure, the look up table is generated based on parameters including (i) a Customer Proximity Score (CPS) indicative of a reach of each of the extracted use cases and thereby an associated technology (ii) an Industry Impact Score (IIS) indicative of the impact of each of the extracted use cases and thereby an associated technology based on fulfilment of core, ancillary or peripheral job in an associated industry, (iii) an Industry Adoptability Score (IAS) indicative of adoption within the industry of each of the extracted use cases and thereby an associated technology, and (iv) Cross-Industry Adaptability Score (CAS) indicative of applicability of each of the extracted use cases and thereby an associated technology to a plurality of industries.

[067] In an embodiment, the CPS is computed using an ecommerce function-based classification of each of the industries such as Business to Consumer (B2C), Business to Business to Consumer (B2B2C), and the like; the IIS score is based on the identified JTBD; the IAS is based on the computed CoT; and the CAS is based on the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries.
[068] Some functions like Digital Marketing are applicable across Industry Domains. The mapping of the emerging technologies to the industries identifies such relations. Classifications like B2C, B2B2C, and the like are maintained in the technology corpus of interest to the enterprise. Using these classifications, the reach of the use case and hence the technology is expressed as the CPS. The CoT is indicative of whether the adoption of a technology within an industry is widespread, normal or niche. If a use case is applicable to several industries, this characteristic is expressed as the CAS.
[069] An exemplary text from the obtained artifacts with the extracted use case, mapped emerging technology and industry is as given below.
Text: Methods Apparatuses And Systems For Radio-frequency Imaging Sensors For Advanced Fingerprint Biometrics And Medical Imaging. Methods apparatuses systems and implementations of an ultra-compact RF 30 GHz-10 THz imaging sensor topology that provides a new insight into the human skin are disclosed. The skin tissue is the largest organ in the body-both in weight and surface area and stores valuable information that can revolutionize security biometrics and mobile health monitoring. The proposed compact sensor enables for the first time portable and wearable devices to perform superior biometric authentication compared to current fingerprint methods. Additionally, these devices could probe into the skin to monitor vital signs in real-time and enable mobile health monitoring. Extracted use case: Enable mobile health monitoring Emerging technology mapped: Wearables Industry mapped: Healthcare
[070] Table 9 below provides an exemplary look up table for measuring future impact using the parameters mentioned above.

Table 9:

Customer Proximity Industry Impact Industry adoptability Cross-Industry Future impact adaptability
B2B2C Peripheral Niche No < 10^6
B2B2C Peripheral Niche Yes < 10^6
B2B2C Peripheral Medium No < 10^6
B2B2C Peripheral Medium Yes < 10^6
B2B2C Peripheral Widespread No 10^6
B2B2C Peripheral Widespread Yes 10^7
B2B2C Ancillary Niche No 10^6
B2B2C Ancillary Niche Yes 10^7
B2B2C Ancillary Medium No 10^6
B2B2C Ancillary Medium Yes 10^7
B2B2C Ancillary Widespread No 10^7
B2B2C Ancillary Widespread Yes 10^8
B2B2C Core Niche No 10^6
B2B2C Core Niche Yes 10^7
B2B2C Core Medium No 10^7
B2B2C Core Medium Yes 10^8
B2B2C Core Widespread No 10^8
B2B2C Core Widespread Yes 10^9
B2C Peripheral Niche No < 10^6
B2C Peripheral Niche Yes 10^6
B2C Peripheral Medium No 10^6
B2C Peripheral Medium Yes 10^7
B2C Peripheral Widespread No 10^7
B2C Peripheral Widespread Yes 10^8
B2C Ancillary Niche No 10^7

B2C Ancillary Niche Yes 10^8
B2C Ancillary Medium No 10^7
B2C Ancillary Medium Yes 10^8
B2C Ancillary Widespread No 10^8
B2C Ancillary Widespread Yes 10^9
B2C Core Niche No 10^7
B2C Core Niche Yes 10^8
B2C Core Medium No 10^8
B2C Core Medium Yes 10^9
B2C Core Widespread No 10^9
B2C Core Widespread Yes > 10^9
[071] Table 10 below indicates a measured future impact using the Table 9 above. Table 10A maps Industry, Technology and use case while Table 10B maps the Technology to the parameters. Table 10A:

Industry Technology Use case
Healthcare Private PaaS Health and versioning of the middleware technology
Healthcare Smart Robots Include medical materials handling hazardous waste materials disposal, prescription filling and delivery, patient care, direct materials
Bank Blockchain in Government Quantify the financial benefits
Healthcare 4D Printing Improve patient outcomes
Healthcare 4D Printing Assembling medical stent may reduce surgery times
Bank Neo Banking Harnessing digital experience, consumers would expect autonomous banking

Energy Nuclear Fusion Produce enough energy
Bank Complex-Event Processing Banking and credit card processing
Energy Grid Provide energy independence, efficiency and
Modernization,
Energy
internet protection during emergencies.
Energy Vehicle to Help renewable energy become a base load
Grid electricity technology
Healthcare Digital Health Encompasses the digitization of medical records, remote care, appointment booking, self-symptom checkers, patient outcome reporting, and many others
Healthcare Digital Health Aid healthcare professionals and their patients manage illnesses and health risks
Healthcare Digital Health Encompasses the digitization of medical records, remote care, appointment booking, self-symptom checkers, patient outcome reporting, and many others
Healthcare Wearables Enable mobile health monitoring
Table 10B:

Technology Industry Customer Cross- Industry Future
impact proximity Industry adaptability adoptability impact
Private PaaS Core B2C No Widespread 10^9
Smart Robots Ancillary B2B No Widespread 10^7

Blockchain in Government Core B2B No Widespread 10^8
4D Printing Core B2C No Widespread 10^9
4D Printing Ancillary B2B No Widespread 10^7
Neo Banking Core B2B No Widespread 10^8
Nuclear Fusion Core B2B No Medium 10^7
Complex-Event Processing Core B2B No Medium 10^7
Grid
Modernization, Energy internet Core B2B No Medium 10^7
Vehicle to Grid Core B2B No Medium 10^7
Digital Health Core B2C No Niche 10^7
Digital Health Core B2C No Niche 10^7
Digital Health Ancillary B2C No Niche 10^7
Wearables Core B2C No Niche 10^8
[072] In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to refine the future impact of each of the one or more emerging technologies, measured at step 206, using a Q-learning method with a current state of each of the emerging technologies being characterized by 4-tuple of scores including the CPS, the IIS, the IAS and the CAS.
[073] The look up table for measuring the future impact, in accordance with the present disclosure as shown in Table 9, may represent a policy table for the Q-learning method. A future impact identified for a technology by domain experts may be compared against that selected from the policy table. The variance is regarded as the reward (or penalty) for the selection and the Q-learning method is employed to assess and precompute the policy so as to optimize returns over time. Each review cycle with the domain experts forms an episode for the Q-Learning method and each episode consists of multiple steps of assigning an impact measure to a technology. A Q-Table is created with 3 columns namely a state tuple, an action

(selection) and a Q-Value containing all state tuples and action combinations with associated Q Values.
[074] The Default Policy is ‘Greedy’ as it seeks to maximize the reward for the next action. As this lock-in of the selection may result in reduced long-term returns, in an embodiment, random actions may be explored from time to time to attempt random actions using an’s-greedy’ policy.
[075] It is known in the art that trends such as societal trends, business trends, industrial trends, and the like, influence technology development favoring certain technologies at the expense of others. For mature technologies, it is possible to observe the influence of such trends on the technology development by analyzing the technology’s performance in markets whose alignment with prevailing trends are well known. For emerging technologies, there is no market yet and hence it is very difficult to systematically measure and analyze these influences. The present disclosure addresses this requirement. Accordingly, in an embodiment of the present disclosure, the one or more hardware processors 104, are configured to determine an alignment of each of the one or more emerging technologies with a set of trends associated with the one or more emerging technologies, using (i) a paragraph vector of each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend from the set of trends and (ii) a sentiment analysis. In an embodiment, this step precedes the step of auto-suggesting a position for each of the one or more emerging technologies in a cell. The set of trends may include societal trends such as climate change, business trends such as mass personalization or industry trends such as energy. In accordance with the present disclosure, the technology corpus of interest to an enterprise stores a curated and ranked list of such trends using associated content obtained by periodic crawling of the plurality of sources.
[076] In an embodiment, the step of determining an alignment of each of the one or more emerging technologies with the set of trends comprises obtaining (i) the set of trends, (ii) related content thereof and (iii) the list of the one or more emerging technologies and the associated artifacts, from the technology corpus of interest to the enterprise. An exemplary use case to explain alignment is provided

hereinafter. A trend under consideration may be ‘energy’ with an exemplary content obtained as shown below.
Trend - energy: The energy industry is the totality of all of the industries involved in the production and sale of energy, including fuel extraction, manufacturing, refining and distribution. Modern society consumes large amounts of fuel, and the energy industry is a crucial part of the infrastructure and maintenance of society in almost all countries. In particular, the energy industry comprises: the fossil fuel industries, which include petroleum industries (oil companies, petroleum refiners, fuel transport and end-user sales at gas stations) coal industries (extraction and processing) and the natural gas industries (natural gas extraction, and coal gas manufacture, as well as distribution and sales); the electrical power industry, including electricity generation, electric power distribution and sales; the nuclear power industry; the renewable energy industry, comprising alternative energy and sustainable energy companies, including those involved in hydroelectric power, wind power, and solar power generation, and the manufacture, distribution and sale of alternative fuels; and, traditional energy industry based on the collection and distribution of firewood, the use of which, for cooking and heating, is particularly common in poorer countries.
The increased dependence during the 20th century on carbon-emitting sources of energy such as fossil fuels, and carbon-emitting renewables such as biomass, means that the energy industry has frequently been an important contributor to pollution and environmental impacts of the economy.
Until recently, fossil fuels were the main source of energy generation in most parts of the world and are a major contributor to global warming and pollution. As part of human adaptation to global warming, many economies are investing in renewable and sustainable energy.
Since the cost of energy has become a significant factor in the performance of economy of societies, management of energy resources has become very crucial. Energy management involves utilizing the available energy resources more effectively; that is, with minimum incremental costs.

Many times, it is possible to save expenditure on energy without incorporating fresh technology by simple management techniques.
Most often energy management is the practice of using energy more efficiently by eliminating energy wastage or to balance justifiable energy demand with appropriate energy supply. The process couples energy awareness with energy conservation.
[077] Two exemplary emerging technologies under consideration may be ‘energy harvesting and storage’ and ‘wireless energy transfer’. Exemplary content obtained for the exemplary emerging technologies in the exemplary use case may be as given below.
Technology- Energy harvesting and storage: The disclosure relates to a self-charging device for energy harvesting and storage. The self-charging device for energy harvesting and storage includes a first electrode a second electrode spaced from the first electrode a solid electrolyte bridging the first electrode and the second electrode and a water absorbing structure. The water absorbing structure is located on the second electrode absorbs water from external environment and transmits the absorbed water to the solid electrolyte. A fiber-shaped electric energy harvesting and storage device includes a substrate having a fiber shape a lithium-ion storage unit disposed to surround the substrate and a plurality of photoelectric conversion units disposed to surround the lithium-ion storage unit. A system and a method for an energy harvesting and storage apparatus including a flexible substrate an energy harvesting device disposed on the flexible substrate the energy harvesting device is configured to convert mechanical energy into electrical energy an energy storage device disposed on the flexible substrate and in electrical communication with the energy harvesting device and configured to receive and store the electrical energy from the energy harvesting device. An integrated energy harvesting and storage device (IEHSD) includes a solar cell (SC) including an active layer between an optically transparent top electrode and a bottom electrode and an energy storage device (SD) secured below the solar cell including a separator between a first electrode and a second electrode. The bottom electrode and the first or second electrode are electrically common with one another and are within a distance of

300m from one another. The storage of solar energy will significantly reduce dependence on fossil fuels. It will also lead to less carbon emission. If the process to generate lithium-ion batteries is more environment friendly it will further reduce carbon pollution.
Technology – Wireless energy transfer: An example operation may include one or more of determining an energy state of a system generating a wireless energy transfer request based on the energy state transmitting the wireless energy transfer request to another system receiving wireless energy transfer information from the other system performing a wireless energy exchange with the other system based on the wireless energy transfer information and receiving a data block associated with the wireless energy exchange from the other system. A wireless energy transfer system includes a first energy transfer unit having at least one resonant frequency a second energy transfer unit having the at least one resonant frequency and a load. The first wireless energy transfer unit includes a first coil magnetically coupled to a first wireless energy transfer cell and the second wireless energy transfer unit includes a second coil magnetically coupled to a second wireless energy transfer cell. The first coil receives first energy and through the magnetic coupling between the first coil and the first wireless energy transfer cell the first wireless energy transfer cell is caused to generate second energy wherein the second wireless energy transfer cell receives the second energy and through the magnetic coupling between the second wireless energy transfer cell and the second coil the second coil is caused to provide third electromagnetic wave energy to the load. A method including controlling a wireless energy transfer apparatus to synchronize energy transfer with at least one further wireless energy transfer apparatus to wirelessly transfer energy to at least one load in combination with the at least one further wireless energy transfer apparatus.
[078] A sentiment score is then computed for each sentence associated with each trend from the set of trends, using the sentiment analysis on the related content. For the exemplary trend ‘energy’ under consideration, some exemplary sentiment scores obtained maybe as described herein below.

Computed sentiment score is (-1) for the sentence: The increased dependence
during the 20th century on carbon-emitting sources of energy such as fossil fuels,
and carbon-emitting renewables such as biomass, means that the energy industry
has frequently been an important contributor to pollution and environmental
impacts of the economy.
Computed sentiment score is (-1) for the sentence: Until recently, fossil fuels were
the main source of energy generation in most parts of the world, and are a major
contributor to global warming and pollution.
Computed sentiment score is (0) for the sentence: As part of human adaptation to
global warming, many economies are investing in renewable and sustainable
energy.
Computed sentiment score is (0) for the sentence: Since the cost of energy has
become a significant factor in the performance of economy of societies,
management of energy resources has become very crucial.
Computed sentiment score is (+1) for the sentence: Energy management involves
utilizing the available energy resources more effectively; that is, with minimum
incremental costs.
Computed sentiment score is (+1) for the sentence: Many times, it is possible to
save expenditure on energy without incorporating fresh technology by simple
management techniques.
Computed sentiment score is (+1) for the sentence: Most often energy management
is the practice of using energy more efficiently by eliminating energy wastage or to
balance justifiable energy demand with appropriate energy supply.
Computed sentiment score is (+1) for the sentence: The process couples energy
awareness with energy conservation.
[079] In accordance with the present disclosure, as part of the step of determining an alignment of each of the one or more emerging technologies with the set of trends, a paragraph vector is then computed for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend using a pretrained Document Vector library. In an embodiment, the pretrained Document vector library is a Deeplearning4J (DL4J) employed to train

a trend evaluation model. Training the trend evaluation model involves computing the paragraph vectors for all the sentences obtained for each trend and each of the one or more emerging technologies and calculating a centroid vector for them. For a trend, if there are say 3 paragraph vectors corresponding to 3 associated artifacts, they are combined using the centroid vector.
[080] In accordance with the present disclosure, as part of the step of determining an alignment of each of the one or more emerging technologies with the set of trends, a first alignment score representing the alignment of each of the one or more emerging technologies with each trend is computed using the computed paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend. To compute the first alignment score, a similarity score between (i) an emerging technology from the one or more emerging technologies and (ii) each sentence associated with a trend is computed.
[081] Using the exemplary trend ‘energy’ and the technology ‘Energy harvesting and storage’, exemplary similarity score computed may be as given below.
Similarity score with Energy Harvesting and Storage =0.42 for the sentence: The increased dependence during the 20th century on carbon-emitting sources of energy such as fossil fuels, and carbon-emitting renewables such as biomass, means that the energy industry has frequently been an important contributor to pollution and environmental impacts of the economy.
Similarity score with Energy Harvesting and Storage = 0.35 for the sentence: Until recently, fossil fuels were the main source of energy generation in most parts of the world, and are a major contributor to global warming and pollution.
Similarity score with Energy Harvesting and Storage = 0.91 for the sentence: As part of human adaptation to global warming, many economies are investing in renewable and sustainable energy.
Similarity score with Energy Harvesting and Storage =0.93 for the sentence: Since the cost of energy has become a significant factor in the performance of economy of societies, management of energy resources has become very crucial.

Similarity score with Energy Harvesting and Storage = 0.97 for the sentence:
Energy management involves utilizing the available energy resources more
effectively; that is, with minimum incremental costs.
Similarity score with Energy Harvesting and Storage =0.74 for the sentence: Many
times it is possible to save expenditure on energy without incorporating fresh
technology by simple management techniques.
Similarity score with Energy Harvesting and Storage = 0.96 for the sentence: Most
often energy management is the practice of using energy more efficiently by
eliminating energy wastage or to balance justifiable energy demand with
appropriate energy supply.
Similarity score with Energy Harvesting and Storage = 0.91 for the sentence: The
process couples energy awareness with energy conservation.
[082] In accordance with the present disclosure, as part of the step of determining an alignment of each of the one or more emerging technologies with the set of trends, once the similarity scores are obtained, only the sentences having a similarity score greater than 90% are considered. Accordingly, a product of (i) the similarity score of each sentence associated with a trend and (ii) the computed sentiment score is computed only for the sentences having a similarity score greater than 90%. Using the exemplary trend ‘energy’ and the technology ‘Energy harvesting and storage’, the exemplary sentiment scores and the exemplary similarity scores computed above, the computed products may be as shown below. Product = 0*0.91 = 0 for the sentence: As part of human adaptation to global warming, many economies are investing in renewable and sustainable energy. Product = 0*0.93 =0 for the sentence: Since the cost of energy has become a significant factor in the performance of economy of societies, management of energy resources has become very crucial.
Product = 1*0.97 =0.97 for the sentence: Energy management involves utilizing the available energy resources more effectively; that is, with minimum incremental costs.

Product = 1*0.96 =0.96 for the sentence: Most often energy management is the practice of using energy more efficiently by eliminating energy wastage or to balance justifiable energy demand with appropriate energy supply.
Product = 1*0.91 =0.91 for the sentence: The process couples energy awareness with energy conservation.
[083] The first alignment score representing the alignment of the emerging technology with the trend is obtained as a summation of the obtained products for the sentences having the similarity score greater than 90%. For the exemplary products computed above, the first alignment score=0+0+0.97+0.96+0.91= 2.84. Similarly, the first alignment score is computed for the exemplary technology ‘wireless energy transfer’ as 1.86.
[084] In accordance an embodiment of the present disclosure, once the alignment of an emerging technology with a trend is obtained, an alignment of a technology group cluster to which the emerging technology belongs can also be obtained. Accordingly, a second alignment score representing the alignment of a technology group cluster comprising the one or more emerging technologies with each trend is computed, wherein the second alignment score is obtained by summing, the first alignment score for each of the one or more emerging technologies, in the technology group cluster, with each trend. It may be noted that the technology corpus of interest to an enterprise comprises the details of grouping of the one or more emerging technologies under a technology group cluster. For the exemplary trend ‘energy’ and the emerging technologies ‘energy harvesting and storage’ and ‘wireless energy transfer’ under a technology group cluster ‘new energy system’, the summation of the first alignment scores resulting results in the second alignment score for the trend ‘energy’ = 2.84+1.86 = 4.7.
[085] In accordance an embodiment of the present disclosure, an alignment rank may be assigned to each technology group cluster corresponding to an associated overall technology group alignment score based on the computed second alignment score for each trend. The second alignment scores may be computed for all the trends in the technology group cluster to obtain the overall technology group alignment score. Continuing the exemplary use case, if the second

alignment score for another exemplary trend ‘climate change’ in the technology group cluster ‘new energy system’ is 3.86, the overall technology group alignment score for the technology group cluster ‘new energy system’ = 4.7+3.86 = 8.56.
[086] An enterprise requires to understand the emerging technologies that are aligned to its areas of interest and further evaluate disruptive innovation opportunities at convergences by selecting a right combination of technologies. State-of-the-art methods rely on examining patent citations and link prediction techniques that do not work reliably on emerging technologies since there is not enough information available at an early stage of evolution. In accordance with the present disclosure, information available at the nascent stage (stored in the technology corpus of interest after periodic crawling of the plurality of sources) is utilized to identify potential convergences.
[087] In accordance with the present disclosure, the technology corpus of interest to an enterprise comprises characteristics of the one or more emerging technologies including (i) a technology group cluster, for the current time period, to which each of the one or more emerging technologies belongs, (ii) a technology family from a group of technology families, to which each technology group cluster belongs, (iii) a computed reactivity of each technology group cluster with other technology group clusters, (iv) an importance rank associated with each of the one or more emerging technologies in a list of emerging technologies. In an embodiment, the importance rank associated with each of the one or more emerging technologies may be computed based on a Content Attribution Score using the method provided in the Applicants’ Patent Application No. 202121036165, wherein the Content Attribution Score is computed for each technology based on associated page rank, percentage of contribution of each of the plurality of sources to each technology, and a source rank associated with each technology.
[088] Further characteristics of the one or more emerging technologies include one or more of (v) the predicted time horizon for each of the one or more emerging technologies obtained from step 204, (vi) a measured future impact of each of the emerging technologies obtained from step 206, and (vii) the obtained alignment rank associated with each of the emerging technologies.

[089] In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to auto-suggest, at step 208, a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more of the characteristics (described above) of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table. The convergence is representative of a degree of collaboration (the characteristic reactivity) of each emerging technology with other emerging technologies. In an embodiment, the temporal representation may be a table comprising 100 cells, wherein the plurality of cells maybe at least one of technology cells associated with the characteristics (i) through (vi) and trend cells associated with the characteristic (vii) mentioned above.
[090] FIG.4 illustrates an exemplary temporal representation of emerging technologies in a table, in accordance with some embodiments of the present disclosure. In FIG.4, using the characteristic (i) being the technology group cluster, the characteristic (ii) being the technology family and the characteristic (v) being the predicted time horizon, an emerging technology UT1H01 is positioned as shown in the table, which indicates that it belongs to a technology group cluster UT1 which in turn belongs to the Unitary technology family, and the emerging technology UT1H01 has a predicted time horizon H01 represented as Horizon H01.
[091] The characteristic (iv), being the importance rank associated with each of the one or more emerging technologies in a list of emerging technologies decides an initial position of a technology in the tabular representation. Accordingly, positions of emerging technologies UT1H01, UT1H02 and UT1H03 are based on associated importance rank and the placement in FIG.4 is indicative of UTIH01 having a higher importance rank than UT1H02 which in turn has a higher importance rank than UT1H03. The emerging technologies in the cells across rows are further based on the predicted time horizon. For instance, in FIG.4,

the placement of UT1H01 and UT1H12 indicates that UT1H01 is predicted to mature faster than UT1H12. Further, the emerging technologies in the cells within each row are filled based on associated characteristic (iii), the computed reactivity. For instance, if there were more than one column under UT1 (on account of more emerging technologies belonging to the technology group cluster), the emerging technologies are placed in the decreasing order of the computed reactivity from left to right of the table. For instance, if UT1aH11 (not shown) and UT1bH11(not shown) are two emerging technologies under the technology group cluster UT1 in the predicted time horizon H1, UT1aH11 placed to the left of UT1bH11 indicates UT1aH11 is associated with a higher computed reactivity than UT1bH11.
[092] In FIG.4, the trend cells associated with business trends are placed in the extreme right column, in a decreasing order of influence. In an embodiment, a score based on voting related to influence of the trend on the industry may be obtained to determine the position of the trend cells. In FIG.4, a business trend BT1 is more influential than a business trend BT2 as indicated by the positions auto-suggested in the table. In FIG.4, trend cells associated with societal trends are also depicted in two rows at the bottom of the table with the degree of influence decreasing from left to right and top to bottom. For instance, a societal trend STA1 is more influential than a societal trend STA2 and likewise societal trends STA1-STA7 are more influential than societal trends STB1-STB7. Similarly, trend cells associated with industry trends may also be depicted.
[093] Having generated a temporal representation of the one or more emerging technologies for the current time period, the enterprise needs further inferences that may be derived from the table. In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to provide , at step 210, an interactive data visualization associated with one or more cells in the plurality of cells, wherein the data visualization provides information pertaining to one or more of (i) the one or more characteristics and (ii) inferences pertaining to the extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts.

[094] FIG.5A through FIG.5D illustrate various exemplary visual indicators that facilitate inferences via the table of FIG.4, in accordance with some embodiments of the present disclosure. In an embodiment, the measured impact score may be presented using visual indicators as shown in FIG.5A. The visual indicators for the exemplary cells having the emerging technologies UT1H01, UT3H03, TD3H11, and ID2H12 represent a decreasing order of the measured future impact. Accordingly, the emerging technology UT1H01 has higher measured future impact than the emerging technology UT3H03 and so on.
[095] In an embodiment, similar visual indicators may be provided based on enterprise activity. For instance, in FIG.5A provides a representation of level of activity of the enterprise in each of the one or more emerging technologies evidenced by the obtained artifacts. The visual indicators for the exemplary cells having the emerging technologies UT1H01, UT3H03, TD3H11, ID2H12 and ID3H2 represent a decreasing order of the measured future impact.
[096] In an embodiment, the first alignment score of an emerging technology with one or more trends can be provided. FIG.5A illustrates the first alignment score of UT1H01, UT3H03, TD3H11, ID2H12 and ID3H2 with a single trend, wherein the visual indicators represent a decreasing value of the first alignment score.
[097] The first alignment score of UT1H01 with two trends is also shown, wherein the inner shade represents an alignment with a first trend and the outer shade represents the alignment with a second trend. In FIG.5A, it may be noted that UT1H01 is more aligned with the first trend than with the second trend.
[098] FIG.5B illustrates an exemplary cell with emerging technology UT3H03. On click of the cell, exemplary characteristics such as a count of artifacts associated with the emerging technology, associated stage of innovation and measured future impact.
[099] FIG.5C illustrates an exemplary knowledge graph that may be provided as part of the interactive data visualization, wherein the knowledge graph pertains to connected use cases, obtained artifacts (such as Gartner reports), connected startups and the connected industries for an emerging technology. In

FIG.5C, a central node represents a technology node and the bigger nodes of other shapes represent a parent connected node to the technology node and the smaller nodes of same shape represent associated child nodes. The thickness of the edge connecting the parent type node and the child node represent the activity count of that child node.
[0100] FIG.5D illustrates intersections pertaining to the extracted use cases, startups and industries associated with two emerging technologies (represented as Technology 1 and Technology 2). In an embodiment, for the selected two emerging technologies, the intersecting (combinatory) use cases are shown initially. In another embodiment, an option to view use cases associated with a single emerging technology is enabled. Upon clicking of any of the use case node, the connected technologies may be viewed, which makes it a multipartite graph with an n level projection. Similar views may be provided for the connected industries and the connected startups.
[0101] In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to refine, at step 212, the position for each of the one or more emerging technologies in a cell, using a Q-learning method.
[0102] In an embodiment, a cascaded Q-Learning method enables simultaneous learning of different stage models such as selecting a cell for an emerging technology, predicting the technology group cluster it belongs to, predicting an importance rank for the emerging technology within the technology group cluster, and predicting the time horizon. When a cell is filled, it simultaneously provides a feedback regarding the emerging technology that was allotted to the cell along with the allotted position. A first observation helps learn the auto-suggestion policy, while the latter helps refine early-stage prediction policies about each emerging technology. The learning happens in a cascaded fashion, wherein a reward from a current allocation to a suggested cell is processed by early-stage models and positions of the emerging technologies are updated, after which the later stage model learns the policy.
[0103] Providing a crisp and temporal representation of emerging technologies with data visualization and inferencing is a challenging task

considering input data is obtained from a plurality of sources (offline, from the web). Besides the sources themselves being subjective, an analysis of the technologies is dependent on subject matter experts which adds to the subjectivity. Inferencing from large volumes of data is also very tedious. Traditional visualization does not permit the much-needed flexibility in analysis of emerging technologies. The present disclosure systematically analyzes the emerging technologies, using the limited data available, to position them in a table based on their characteristics and convergence capabilities. The position of the emerging technologies in the table provides quick insights on associated characteristics and also enables interactive data visualization with the flexibility to try out various combinatory use cases and identify the best fit to meet objectives of the enterprise.
[0104] In accordance with the present disclosure, the predicted time horizon for each emerging technology is indicative of a timeline when the technology will reach a certain maturity. Further, each emerging technology is analyzed to determine if it is constructively aligned, destructively aligned or completely unaligned with observed trends. Rather than nudging the technology into greater compliance, the present disclosure provides inferences on the growth and impact of the technology due to its casual alignment with one or more trends. Furthermore, in accordance with the present disclosure, the future impact is measured in terms of number of end users it will impact, which is particularly relevant considering the technologies under consideration are emerging technologies and currently in a nascent stage of evolution. After the analysis, the method and system of the present disclosure organizes the information into a meaningfully structured table for display and stores them as a knowledge graph. Multipartite graphs extracted from knowledge graph are employed to enable interactive data visualization by exploring intersection of two or more emerging technologies and discovering use cases with exponential disruptive capabilities (future impact).
[0105] In an embodiment of the present disclosure, the one or more hardware processors 104, are configured to input, at step 214, (i) the generated table into at least one of (i) a business transformation tool to generate transformation

steps to be executed and (ii) an enterprise-wide search engine for identifying solutions using emerging technologies.
[0106] In an embodiment, the business transformation tool may be TCS MasterCraft™. Today it is pertinent that any business is future ready to not just thrive but also to survive. With business requirements changing frequently, and adopting digital technologies becoming a norm, assessing collaborative capabilities of the emerging technologies and generating specific transformation steps is the need of the hour. The system and method of the present disclosure can find application in such tools where the generated table and inferences can provide objective and consistent inputs obtained in a systematic and comprehensive manner.
[0107] In another embodiment, the system and method of the present disclosure can also find application in enterprise-wide search engine such as SharePoint™ search engine. The wide knowledgebase of SharePoint™ may be used as an add-on to the technology corpus of interest of the present disclosure to extract use cases and emerging technologies with additional parameters such as the predicted time horizon, measured future impact and its position in the table. The enriched outcome may then me utilized by the SharePoint™ search engine to derive enhanced inferences.
[0108] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0109] 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 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.
[0110] 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.
[0111] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms 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.
[0112] 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 hardware 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.
[0113] 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.

We Claim:
1. A processor implemented method (200) comprising the steps of:
obtaining, via one or more hardware processors, a plurality of artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from a technology corpus of interest to an enterprise, wherein the one or more technologies of interest are one or more emerging technologies for a current time period (202);
predicting, via the one or more hardware processors, a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries (204);
measuring, via the one or more hardware processors, a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts (206); and
auto-suggesting, via the one or more hardware processors, a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more characteristics of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table (208).

2. The processor implemented method of claim 1, wherein the step of
predicting a time horizon for each of the one or more emerging technologies comprises:
classifying the one or more emerging technologies into an associated stage of innovation based on the obtained artifacts, wherein the associated stage of innovation is one of (i) basic research evidenced by the obtained abstracts of research papers, (ii) applied research evidenced by the obtained abstracts of research papers and the patent documents, and (iii) technology development evidenced by startup launch and new product, service, Line Of Business announcements;
plotting the obtained artifacts associated with each of the one or more emerging technologies classified under each stage of innovation over a predefined timeline;
identifying on the predefined timeline, a level of activity associated with each of the one or more emerging technologies classified under each stage of innovation, based on volume, citation or funds associated with each of the one or more emerging technologies;
computing, the CoT based on the level of activity associated with each of the one or more emerging technologies, using a Bass Diffusion model, the CoT being an indicator for the slope of a Bass curve and is computed as a weighted aggregation of the of the yearly total activity levels for the predefined timeline; the TDL is based on number of startups identified, from the obtained artifacts, having a Series A funding greater than a predetermined amount and is indicative of a position of each of the one or more emerging technologies on the Bass curve; and the TVL is based on a percentage of startups which received Series B funding identified from the obtained artifacts; and
predicting the time horizon for each of the one or more emerging technologies based on a mapping of the overall technology score with a time horizon on the predefined timeline.

3. The processor implemented method of claim 1, wherein the step of
measuring a future impact of each of the one or more emerging technologies on end users comprises:
extracting entities comprising (i) the one or more emerging technologies, (ii) the industries, and (iii) uses cases from the obtained artifacts using a pretrained Named Entity Recognition (NER) model;
performing the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries, wherein a first level mapping is performed between the extracted use cases and one or more emerging technologies using an Natural Language Processing (NLP) parser to resolve semantic relationships between the extracted entities; and a second level mapping is performed between each of the extracted use cases previously mapped to the one or more emerging technologies and one or more of the industries, based on one or more of (i) identified keywords for each of the industries, (ii) industry core and ancillary Jobs to be Done (JTBD) identified from a taxonomy of industries, and (iii) industry digital Jobs to be Done (D-JTBD) identified from reports on industry-wise digitization scope; and
measuring the future impact of each of the one or more emerging technologies on the end users using a look up table on the mapped extracted use cases, the one or more emerging technologies and the industries, wherein the look up table is generated based on parameters including (i) a Customer Proximity Score (CPS) indicative of a reach of each of the extracted use cases and thereby an associated technology (ii) an Industry Impact Score (IIS) indicative of the impact of each of the extracted use cases and thereby an associated technology based on fulfilment of core, ancillary or peripheral job in an associated industry, (iii) an Industry Adoptability Score (IAS) indicative of adoption within the industry of each of the extracted use cases and thereby an associated technology, and (iv) Cross-Industry Adaptability Score (CAS) indicative of applicability of each of the

extracted use cases and thereby an associated technology to a plurality of industries.
4. The processor implemented method of claim 3, wherein the identified
keywords pertaining to each of the industries are obtained using a network
analysis comprising:
extracting descriptive content for each of the industries from the obtained artifacts;
extracting potential keywords related to each of the industries using a Term frequency-inverse document frequency (TF-IDF) measure;
generating a network with the extracted potential keywords as nodes and co-occurrence of the extracted keywords in a sentence as an edge; and
identifying a predefined number of keywords from the extracted potential keywords, for each of the industries using a Weighted Degree Centrality (WDC) associated with the generated graph.
5. The processor implemented method of claim 4, wherein (i) the CPS is computed using an ecommerce function-based classification of each of the industries, (ii) the IIS score is based on the identified JTBD (iii) the IAS is based on the computed CoT, and (iv) the CAS is based on the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries.
6. The processor implemented method of claim 5, further comprising refining the measured future impact of each of the one or more emerging technologies using a Q-learning method with a current state of each of the emerging technologies being characterized by 4-tuple of scores including the CPS, the IIS, the IAS and the CAS.
7. The processor implemented method of claim 1, wherein the step of auto-suggesting a position for each of the one or more emerging technologies in

a cell is preceded by determining, an alignment of each of the one or more emerging technologies with a set of trends associated with the one or more emerging technologies, using (i) a paragraph vector of each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend from the set of trends and (ii) a sentiment analysis.
8. The processor implemented method of claim 7, wherein the step of
determining an alignment of each of the one or more emerging technologies with a set of trends comprises:
obtaining (i) the set of trends, (ii) related content thereof and (iii) the list of the one or more emerging technologies and the associated artifacts, from the technology corpus of interest to the enterprise;
computing a sentiment score for each sentence associated with each trend from the set of trends, using the sentiment analysis on the related content;
computing a paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend using a pretrained Document Vector library;
computing one or more of (i) a first alignment score representing the alignment of each of the one or more emerging technologies with each trend using the computed paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend, and (ii) a second alignment score representing the alignment of a technology group cluster comprising the one or more emerging technologies with each trend, wherein the second alignment score is obtained by summing, the first alignment score for each of the one or more emerging technologies, in the technology group cluster, with each trend; and
assigning an alignment rank to each technology group cluster corresponding to an associated overall technology group alignment score based on the computed second alignment score for each trend.

9. The processor implemented method of claim 8, wherein the step of
computing the first alignment score comprises performing, for each of the
one or more emerging technologies with each trend, the steps of:
computing a similarity score between (i) an emerging technology from the one or more emerging technologies and (ii) each sentence associated with a trend;
obtaining a product of (i) the similarity score of each sentence associated with a trend and (ii) the computed sentiment score, only for the sentences having a similarity score greater than 90%; and
obtaining the first alignment score representing the alignment of the emerging technology with the trend, as a summation of the obtained products for the sentences having the similarity score greater than 90%.
10. The processor implemented method of claim 1, wherein the step of auto-
suggesting, a position for each of the one or more emerging technologies in
a cell amongst a plurality of cells is preceded by receiving, from the
technology corpus of interest to an enterprise, one or more characteristics of
the one or more emerging technologies including (i) a technology group
cluster, for the current time period, to which each of the one or more
emerging technologies belongs, (ii) a technology family from a group of
technology families, to which each technology group cluster belongs, (iii) a
computed reactivity of each technology group cluster with other technology
group clusters, (iv) an importance rank associated with each of the one or
more emerging technologies in a list of emerging technologies; and further
characteristics of the one or more emerging technologies including one or
more of (v) the predicted time horizon for each of the one or more emerging
technologies, (vi) a measured future impact of each of the emerging
technologies, and (vii) the alignment rank associated with each of the
emerging technologies.

11. The processor implemented method of claim 7, further comprising providing, via the one or more hardware processors, an interactive data visualization associated with one or more cells in the plurality of cells, wherein the data visualization provides information pertaining to one or more of (i) the one or more characteristics and (ii) inferences pertaining to the extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts (210).
12. The processor implemented method of claim 1, further comprising refining, via the one or more hardware processors, the position for each of the one or more emerging technologies in a cell, using a Q-learning method (212).
13. The processor implemented method of claim 1 further comprising inputting, via the one or more hardware processors, the generated table into at least one of (i) a business transformation tool to generate transformation steps to be executed and (ii) an enterprise-wide search engine for identifying solutions using emerging technologies (214).
14. A system (100) comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the
one or more communication interfaces (106), wherein the one or more
hardware processors (104) are configured by the instructions to:
obtain a plurality of artifacts comprising (i) at least abstracts of research papers, (ii) at least abstracts of patent documents, and (iii) literature pertaining to one or more technologies of interest to the enterprise, from a technology corpus of interest to an enterprise, wherein the one or more technologies of interest are one or more emerging technologies for a current time period;

predict a time horizon for each of the one or more emerging technologies using the obtained artifacts and an overall technology score based on a computed (i) Cost of Technology (CoT) indicative of time needed for adoption of each of the one or more emerging technologies by industries, (ii) a development lead time (TDL) indicative of time needed for development and (iii) a validation lead time (TVL) indicative of validation of each of the one or more emerging technologies by the industries;
measure a future impact of each of the one or more emerging technologies on end users, using (i) the computed CoT and (ii) a two-level mapping between extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts; and
auto-suggest a position for each of the one or more emerging technologies in a cell amongst a plurality of cells, in a ranked order, based on (i) one or more characteristics of an associated emerging technology derived from the obtained artifacts and (ii) convergence of the associated emerging technology with other emerging technologies from the one or more emerging technologies, to generate a temporal representation of the one or more emerging technologies for the current time period, wherein the temporal representation is an array of the plurality of cells forming a table.
15. The system of claim 14, wherein the one or more processors are configured
to predict a time horizon for each of the one or more emerging technologies by:
classifying the one or more emerging technologies into an associated stage of innovation based on the obtained artifacts, wherein the associated stage of innovation is one of (i) basic research evidenced by the obtained abstracts of research papers, (ii) applied research evidenced by the obtained abstracts of research papers and the patent documents, and (iii) technology development evidenced by startup launch and new product, service, Line Of Business announcements;

plotting the obtained artifacts associated with each of the one or more emerging technologies classified under each stage of innovation over a predefined timeline;
identifying on the predefined timeline, a level of activity associated with each of the one or more emerging technologies classified under each stage of innovation, based on volume, citation or funds associated with each of the one or more emerging technologies;
computing, the CoT based on the level of activity associated with each of the one or more emerging technologies, using a Bass Diffusion model, the CoT being an indicator for the slope of a Bass curve and is computed as a weighted aggregation of the of the yearly total activity levels for the predefined timeline; the TDL is based on number of startups identified, from the obtained artifacts, having a Series A funding greater than a predetermined amount and is indicative of a position of each of the one or more emerging technologies on the Bass curve; and the TVL is based on a percentage of startups which received Series B funding identified from the obtained artifacts; and
predicting the time horizon for each of the one or more emerging technologies based on a mapping of the overall technology score with a time horizon on the predefined timeline.
16. The system of claim 14, wherein the one or more processors are configured
to measure a future impact of each of the one or more emerging technologies on end users by:
extracting entities comprising (i) the one or more emerging technologies, (ii) the industries, and (iii) uses cases from the obtained artifacts using a pretrained Named Entity Recognition (NER) model;
performing the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries, wherein a first level mapping is performed between the extracted use cases and one or more emerging technologies using an Natural Language Processing (NLP) parser

to resolve semantic relationships between the extracted entities; and a second level mapping is performed between each of the extracted use cases previously mapped to the one or more emerging technologies and one or more of the industries, based on one or more of (i) identified keywords for each of the industries, (ii) industry core and ancillary Jobs to be Done (JTBD) identified from a taxonomy of industries, and (iii) industry digital Jobs to be Done (D-JTBD) identified from reports on industry-wise digitization scope; and
measuring the future impact of each of the one or more emerging technologies on the end users using a look up table on the mapped extracted use cases, the one or more emerging technologies and the industries, wherein the look up table is generated based on parameters including (i) a Customer Proximity Score (CPS) indicative of a reach of each of the extracted use cases and thereby an associated technology (ii) an Industry Impact Score (IIS) indicative of the impact of each of the extracted use cases and thereby an associated technology based on fulfilment of core, ancillary or peripheral job in an associated industry, (iii) an Industry Adoptability Score (IAS) indicative of adoption within the industry of each of the extracted use cases and thereby an associated technology, and (iv) Cross-Industry Adaptability Score (CAS) indicative of applicability of each of the extracted use cases and thereby an associated technology to a plurality of industries.
17. The system of claim 16, wherein the identified keywords pertaining to each
of the industries are obtained using a network analysis comprising:
extracting descriptive content for each of the industries from the obtained artifacts;
extracting potential keywords related to each of the industries using a Term frequency-inverse document frequency (TF-IDF) measure;
generating a network with the extracted potential keywords as nodes and co-occurrence of the extracted keywords in a sentence as an edge; and

identifying a predefined number of keywords from the extracted potential keywords, for each of the industries using a Weighted Degree Centrality (WDC) associated with the generated graph.
18. The system of claim 17, wherein (i) the CPS is computed using an ecommerce function-based classification of each of the industries, (ii) the IIS score is based on the identified JTBD (iii) the IAS is based on the computed CoT, and (iv) the CAS is based on the two-level mapping between the extracted use cases, the one or more emerging technologies and the industries.
19. The system of claim 18, wherein the one or more processors are configured to refine the measured future impact of each of the one or more emerging technologies using a Q-learning method with a current state of each of the emerging technologies being characterized by 4-tuple of scores including the CPS, the IIS, the IAS and the CAS.
20. The system of claim 14, wherein the one or more processors are configured to determine, an alignment of each of the one or more emerging technologies with a set of trends associated with the one or more emerging technologies, using (i) a paragraph vector of each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend from the set of trends and (ii) a sentiment analysis; prior to auto-suggesting a position for each of the one or more emerging technologies in a cell.
21. The system of claim 20, wherein the one or more processors are configured to determine an alignment of each of the one or more emerging technologies with a set of trends by:

obtaining (i) the set of trends, (ii) related content thereof and (iii) the list of the one or more emerging technologies and the associated artifacts, from the technology corpus of interest to the enterprise;
computing a sentiment score for each sentence associated with each trend from the set of trends, using the sentiment analysis on the related content;
computing a paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend using a pretrained Document Vector library;
computing one or more of (i) a first alignment score representing the alignment of each of the one or more emerging technologies with each trend using the computed paragraph vector for each sentence associated with each of the one or more emerging technologies and each sentence associated with each trend, and (ii) a second alignment score representing the alignment of a technology group cluster comprising the one or more emerging technologies with each trend, wherein the second alignment score is obtained by summing, the first alignment score for each of the one or more emerging technologies, in the technology group cluster, with each trend; and
assigning an alignment rank to each technology group cluster corresponding to an associated overall technology group alignment score based on the computed second alignment score for each trend.
22. The system of claim 21, wherein the one or more processors are configured
to compute the first alignment score by performing, for each of the one or more emerging technologies with each trend, the steps of:
computing a similarity score between (i) an emerging technology from the one or more emerging technologies and (ii) each sentence associated with a trend;
obtaining a product of (i) the similarity score of each sentence associated with a trend and (ii) the computed sentiment score, only for the sentences having a similarity score greater than 90%; and

obtaining the first alignment score representing the alignment of the emerging technology with the trend, as a summation of the obtained products for the sentences having the similarity score greater than 90%.
23. The system of claim 14, wherein the one or more processors are configured to receive, from the technology corpus of interest to an enterprise, one or more characteristics of the one or more emerging technologies including (i) a technology group cluster, for the current time period, to which each of the one or more emerging technologies belongs, (ii) a technology family from a group of technology families, to which each technology group cluster belongs, (iii) a computed reactivity of each technology group cluster with other technology group clusters, (iv) an importance rank associated with each of the one or more emerging technologies in a list of emerging technologies; and further characteristics of the one or more emerging technologies including one or more of (v) the predicted time horizon for each of the one or more emerging technologies, (vi) a measured future impact of each of the emerging technologies, and (vii) the alignment rank associated with each of the emerging technologies; prior to auto-suggesting, a position for each of the one or more emerging technologies in a cell amongst a plurality of cells.
24. The system of claim 20, wherein the one or more processors are configured to provide an interactive data visualization associated with one or more cells in the plurality of cells, wherein the data visualization provides information pertaining to one or more of (i) the one or more characteristics and (ii) inferences pertaining to the extracted use cases, the one or more emerging technologies and the industries extracted from the obtained artifacts.

26. The system of claim 14, wherein the one or more processors are configured
to input the generated table into at least one of (i) a business transformation tool to generate transformation steps to be executed and (ii) an enterprise-wide search engine for identifying solutions using emerging technologies.