Description:FIELD OF INVENTION
The present invention generally relates to a scoring system and method. More particularly, the invention pertains to a system and method for scoring technology maturity in enterprise products and applications; that analyzes and quantifies the alignment of a product’s technology stack with optimal technology stacks derived from fine-tuned large language models (LLMs), preserving consistency across diverse technology domains.

BACKGROUND
The rapid evolution of technologies often leaves organizations struggling to evaluate the maturity of their products and applications. Technology maturity, as it pertains to enterprise systems, is a critical determinant of scalability, performance, and alignment with evolving industry standards. Accurate assessment of technology maturity facilitates informed decision-making regarding upgrades and optimizations. However, conventional methods for evaluating technology maturity lack standardization, objectivity, and consistency, thereby failing to meet the demands of modern enterprise environments. These deficiencies underscore the need for an improved system capable of objectively quantifying technology maturity across diverse technological domains.
Traditionally, technology maturity assessments were performed manually, relying on expert opinions and qualitative measures. These methods were time-consuming and often led to inconsistent evaluations due to varying expertise levels and biases. Additionally, traditional approaches lacked the ability to adapt to the rapid changes in the technology landscape. The absence of an automated, data-driven scoring mechanism to assess the maturity of programming languages, frameworks, and tools exacerbated the challenge. This inadequacy has created a pressing need for a comprehensive system that leverages advanced techniques, such as machine learning and large language models, to provide accurate, objective, and real-time insights into technology maturity across diverse domains.
PRIOR ART
US20140201714A1 provides a system for analyzing data structures and enabling dynamic transformations across environments. While this patent focuses on adaptability within data ecosystems, it does not address the systematic scoring of technology maturity based on fine-tuned LLMs, limiting its applicability to incremental optimizations rather than holistic benchmarking.
US11288166B2 discusses modular systems for scoring and analyzing digital frameworks against benchmark standards. Although it aligns with technology scoring, it lacks the layered approach of leveraging LLMs fine-tuned on high-rated repositories to create a reference stack, making its applicability broader but less precise for maturity assessments.
Thus, the present invention addresses the need for a comprehensive system to objectively evaluate and benchmark technology maturity, enabling informed decisions through consistent and LLM-driven assessments.

DEFINITIONS:
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but is not limited to an scoring system with a user, input and output devices, processing unit, plurality of mobile devices, a mobile device-based application to collect and auto- analyse data, a visualization platform, and output; and is extended to computing systems like mobile, laptops, computers, PCs, etc.
The expression “input unit” used hereinafter in this specification refers to, but is not limited to, mobile, laptops, computers, PCs, keyboards, mouse, pen drives or drives.
The expression “output unit” used hereinafter in this specification refers to, but is not limited to, an onboard output device, a user interface (UI), a display kit, a local display, a screen, a dashboard, or a visualization platform enabling the user to visualize, observe or analyse any data or scores provided by the system.
The expression “processing unit” refers to, but is not limited to, a processor of at least one computing device that optimizes the scoring system.

OBJECTS OF THE INVENTION:
The primary object of the present invention is to provide a system and method for scoring technology maturity in enterprise products and applications.
Another object of the present invention is to enable objective and standardized evaluation of technology stacks across diverse programming languages, frameworks, and algorithms.
Yet another object of the present invention is to identify and quantify deviations between a product's technology stack and an optimal technology stack derived from data-driven models.
Yet another object of the present invention is to provide actionable insights and recommendations for upgrading outdated or underperforming components within a technology stack.
Further, the object of the present invention is to ensure consistency and transparency in technology maturity scoring using automated and scalable methods powered by machine learning models.

SUMMARY
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention discloses a system and method for determining the technology maturity of enterprise products and applications by evaluating their technology stacks against an optimal reference model. The invention addresses the limitations of inconsistent and subjective evaluations by employing a structured approach to standardize and quantify maturity across diverse technologies.
The system identifies and catalogues technology stack components, such as programming languages, frameworks, libraries, and tools, using advanced parsing techniques. It utilizes fine-tuned large language models (LLMs) trained on curated, high-rated repositories to establish a benchmark reference technology model. The maturity of each component is assessed using metrics like Popularity Drift, Performance Drift, and Compatibility Drift, ensuring contextual accuracy and consistency. The scores are aggregated into a unified Technology Maturity Score through a weighted formula, providing a comprehensive evaluation of the product's technological maturity.
The present invention further includes an output generation mechanism that delivers detailed reports, offering insights into component-level maturity, identifying areas for improvement, and recommending targeted optimizations. By ensuring continuous alignment with evolving industry standards, this scalable and adaptable system enables enterprises to enhance product efficiency and maintain technological competitiveness.

BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed descriptions which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
FIG. 1 illustrates an overview of the system of the present invention.
FIG. 2 illustrates the components of the system for scoring technology maturity in enterprise products and applications.
FIG. 3 illustrates the method for Technology Stack Identification Module.
FIG. 4 illustrates the method for Reference Technology Model Generation Module.
FIG. 5 illustrates the method for Drift Calculation Module.
FIG. 6 illustrates the method for the Scoring Module.

DETAILED DESCRIPTION OF INVENTION:
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.
To understand the invention clearly, the various components of the system are referred as below:
No. Component
10 System
100 Input unit
200 Processing unit
300 Output unit
210 Technology Stack Identification Module
220 Reference Technology Model Generation Module
230 Drift Calculation Module
240 Scoring Module
250 Output Generation Module
The present invention is directed to a system and method for scoring technology maturity in enterprise products and applications, wherein the system (10) comprises of at least one input unit (100), a processing unit (200) further comprising of a technology stack identification module (210), a reference technology model generation module (220), a drift calculation module (230), a scoring module (240), an output generation module (250), and at least one output unit (300). The system operates to identify the technological components of a product, compare them with a reference technology stack derived from fine-tuned large language models trained on high-rated repositories, and calculate deviation metrics such as popularity, performance, and compatibility drifts. These drifts are aggregated using a defined formula to generate a technology maturity score, facilitating a consistent and objective evaluation across diverse programming languages, frameworks, and algorithms. The system enables enterprises to make informed decisions on optimizing and upgrading their technology stacks by identifying areas of technological drift and providing actionable insights for enhancement.
According to a preferred embodiment, the technology stack identification module (210) acts as the initial component for cataloging the technology stack of enterprise products and applications; wherein the module (210) analyzes and organizes key components such as programming languages, frameworks, libraries, algorithms, and tools utilized in the product's development and operation. By systematically documenting the said information, the module (210) ensures a structured and comprehensive understanding of the product's technological foundation, forming the basis for subsequent evaluation.
According to another embodiment, the reference technology model generation module (220) creates a benchmark technology stack derived from curated, high-rated repositories; where the module (220) utilizes fine-tuned large language models to extract and rank mature and widely adopted technologies. It generates a weighted reference model that serves as a standard for comparison, enabling the system to objectively assess the product’s technology maturity against industry-best practices.
According to yet another embodiment, the drift calculation module (230) evaluates the deviations of the product’s technology stack from the reference technology model; where the module (230) calculates specific metrics, including popularity drift, performance drift, and compatibility drift, providing a detailed analysis of areas where the product’s stack lags behind the benchmark. These metrics are normalized and aggregated, ensuring consistency and precision in quantifying technological gaps.
According to a further embodiment, the scoring module (240) aggregates the drift metrics into a single comprehensive technology maturity score; wherein the module (240) applies a predefined formula that assigns weighted significance to each drift metric based on its relevance. The system (10) allows the user to modify or edit the formula as per the requirement, thereby seeking confirmation from the user to use the computed formula; whereby the resulting score, ranging from 1 to 100, provides a standardized and actionable metric, enabling enterprises to identify areas requiring technological updates or optimizations.
In yet another embodiment, the output generation module (250) of the system (10) generates actionable insights by generating detailed reports that break down component-level scores and offer recommendations for improvements. These reports aid administrators and decision-makers in prioritizing technology upgrades, aligning product development strategies with current industry standards. This feature ensures the system not only evaluates but also supports continuous improvement of enterprise products and applications.
In another preferred embodiment of the invention, a method for scoring technology maturity in enterprise products and applications enabling the system to evaluate, analyse, and derive insights into the technological maturity of products and applications is disclosed. The method comprises the following steps:
1. Technology Stack Identification Module (210):
The present module initiates the process by systematically parsing the structural composition of the product, including, but not limited to, source code, architectural documentation, and associated dependencies. The module is configured to identify and extract critical components such as programming languages, frameworks, libraries, algorithms, and tools. These components are subsequently organized into a structured representation, such as a tabular format or a dataset, thereby facilitating efficient comparison and downstream analysis. The structured catalog thus generated is outputted as an input for subsequent modules.
2. Reference Technology Model Generation Module (220):
The module is operable to generate a reference technology model based on data sourced from curated repositories, including high-rated GitHub repositories and other authoritative datasets. The process comprises the steps of: (a) collecting data from repositories exhibiting significant community endorsement and adoption metrics; (b) preprocessing the collected data to filter out obsolete, redundant, or irrelevant information; and (c) fine-tuning transformer-based models using the curated datasets. The resulting reference technology model is represented as a weighted list or a knowledge graph, encapsulating critical metadata such as programming languages, dependencies, and implementation techniques. The generated reference model is outputted for benchmarking purposes.
3. Drift Calculation Module (230):
The drift calculation module is configured to evaluate the components of the extracted technology stack in relation to the reference model, thereby computing a maturity score for each component. This process involves determining: (a) Popularity Drift (PD), representing adoption discrepancies relative to community benchmarks; (b) Performance Drift (PerfD), indicative of deviations from reference performance metrics; and (c) Compatibility Drift (CD), measuring adaptability and ease of integration with contemporary technologies. The module normalizes these drift scores using predefined algorithms to ensure consistency across diverse components and outputs the computed drift scores for all elements of the technology stack.
4. Scoring Module (240):
The scoring module is adapted to compute a comprehensive Technology Maturity Score (TMS) based on the results of the drift analysis. This module operates by: (a) receiving the technology stack and the reference model as inputs; (b) performing drift analysis using statistical methods; (c) applying a scoring formula to integrate and normalize individual drift scores; and (d) generating a final TMS, expressed on a scale of 1 to 100. The TMS provides an aggregated measure of the technological maturity of the product, serving as an indicator of the product's alignment with industry standards and benchmarks.
5. Output Generation Module (250):
The output generation module is configured to produce a detailed evaluation report, which includes the final TMS along with a breakdown of the maturity scores for individual components. Additionally, the module provides tailored recommendations for components exhibiting suboptimal scores, outlining actionable steps to enhance their alignment with established maturity benchmarks. The report serves as a critical tool for stakeholders, enabling targeted improvements in the overall technological framework.

The present invention provides several advantages, as it transforms the evaluation of technology maturity in enterprise products and applications by introducing a structured, feature-driven scoring approach instead of relying on subjective or inconsistent assessments. Through its components, such as the technology stack identification module (210), reference technology model generation module (220), drift calculation framework module (230), score aggregation (240) methodology, and the output generation module (250); the invention ensures precise evaluation, actionable insights, and consistent benchmarking across diverse technologies. This approach addresses the challenges of traditional evaluation methods, offering a robust solution for modern enterprises to assess and optimize their technological frameworks effectively.

WORKING EXAMPLE-
An exemplary illustration is provided hereinbelow; in order to clearly understand the method employed by the present system (10) for calculating score. The stepwise workflow is given henceforth.
1. The Technology Stack Identification Module (210) systematically parses the structural composition of the product to extract its components, identifying Python 3.6, Django 2.2, PostgreSQL 10, Jenkins for CI/CD, and Selenium for testing. These components are structured into a dataset, enabling efficient comparison and downstream analysis.
2. The Reference Technology Model Generation Module (220) generates a reference technology model by processing data from curated repositories such as high-rated GitHub projects. The module fine-tunes transformer-based models to produce a benchmark stack comprising Python 3.9, FastAPI, PostgreSQL 14, GitHub Actions, and Cypress. This model encapsulates weighted metadata prioritizing maturity, community adoption, and performance standards.
3. The Drift Calculation Module (230) calculates the deviation (drift) for each extracted component against the reference model by evaluating:
● Popularity Drift (PD): Adoption discrepancies relative to community benchmarks.
● Performance Drift (PerfD): Variations in performance metrics from the reference stack.
● Compatibility Drift (CD): Ease of integration with contemporary technologies.
For example:
● Python 3.6:
PD=0.3, PerfD=0.2, CD=0.1
MS=1−(0.4×PD+0.4×PerfD+0.2×CD) =1−(0.4×0.3+0.4×0.2+0.2×0.1) =1−0.2=0.8.
● Django 2.2:
PD=0.4, PerfD=0.3, CD=0.2
MS=1−(0.4×0.4+0.4×0.3+0.2×0.2) =1−0.32=0.68.
● PostgreSQL 10:
PD=0.2, PerfD=0.1, CD=0.1
MS=1−(0.4×0.2+0.4×0.1+0.2×0.1) =1−0.12=0.88.
4. The Scoring Module (240) aggregates these maturity scores to compute a comprehensive Technology Maturity Score (TMS) using the formula:
TMS=(∑Wi∑(MSi×Wi)) ×100.
However, the system allows the user to modify or edit the formula as per the requirement, thereby seeking confirmation from the user to use the computed formula.
Assuming equal weights (Wi=1):
TMS= (1+1+1(0.8×1) +(0.68×1) +(0.88×1)) ×100 = (32.36) ×100 =78.67.
5. The Output Generation Module (250) generates a detailed evaluation report, presenting the TMS as 78.67/100. The report includes a breakdown of component-wise maturity scores and recommends upgrades such as migrating from Python 3.6 to Python 3.9 and Django 2.2 to FastAPI, enhancing the stack's alignment with established maturity benchmarks. This structured evaluation aids stakeholders in making targeted improvements to the product's technology framework.

While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation. , Claims:CLAIMS:
We claim,
1. A system and method for scoring technology maturity in enterprise products and applications, wherein the system comprises of an input unit (100), a processing unit (200) further comprising of a technology stack identification module (210), a reference technology model generation module (220), a drift calculation module (230), a scoring module (240), and an output generation module (250), and an output unit (300);
characterized in that:
the technology stack identification module (210) identifies and catalogs the product’s technology stack components, including programming languages, frameworks, libraries, algorithms, and tools, in a structured format to ensure compatibility with subsequent analysis processes;
the reference technology model generation module (220) uses fine-tuned large language models trained on curated high-rated repositories to establish a benchmark reference technology stack, characterized by weighted rankings based on maturity, adoption, and performance metrics;
the drift calculation module (230) computes deviation scores for each technology stack component by quantifying differences in metrics such as popularity drift, performance drift, and compatibility drift, ensuring normalized and consistent maturity scoring across diverse domains;
the scoring module (240) aggregates the computed maturity scores using a weighted formula wherein the user is enabled to modify or edit the formula, to generate a unified technology maturity score that reflects the overall maturity of the product’s technology stack;
the output generation module (250) generates comprehensive reports detailing the technology maturity score, including component-level maturity analysis and actionable recommendations for optimizing outdated or underperforming components.

2. The system as claimed in claim 1, wherein the technology stack identification module (210) integrates data parsing methods to extract critical components from source code, architecture documentation, and dependencies, ensuring accuracy and completeness in cataloging the technology stack.

3. The system as claimed in claim 1, wherein the reference technology model generation module (220) employs graph-based methodologies to create knowledge representations of mature technology stacks, incorporating real-time updates for maintaining benchmark accuracy and relevance.

4. The system as claimed in claim 1, wherein the drift calculation module (230) normalizes maturity scores across varying technology domains by applying predefined weights, ensuring fair comparison between components such as programming languages and frameworks.

5. The system as claimed in claim 1, wherein the scoring module (240) utilizes advanced aggregation techniques to combine component maturity scores, generating a comprehensive and standardized maturity score for cross-industry applicability.

6. The system as claimed in claim 1, wherein the output generation module (250) provides component-specific maturity breakdowns and recommendations for technological upgrades, facilitating informed decision-making for optimizing product development processes.

7. The system as claimed in claim 1, wherein the reports generated by the output generation module (250) include visualizations of component drifts and projected impacts of recommended optimizations on the overall technology maturity score.

8. The method as claimed in claim 1, wherein the components of the processing unit (200) employs a method comprising the steps of:
A. technology stack identification module (210)
- parsing source code, architecture documents, and dependencies of the product.
- extracting components such as programming languages, frameworks, libraries, algorithms, and tools.
- storing the extracted components in a structured format
- outputting the cataloged technology stack for further comparison
B. reference technology model generation module (220)
- collecting data from high-rated github repositories and extracting metadata.
- preprocessing the collected data by removing outdated or redundant information.
- fine-tuning transformer-based models using curated datasets.
- generating the reference technology stack as a weighted list or knowledge graph.
- outputting the refined reference model for benchmarking.
C. drift calculation module (230)
- inputting the product’s technology stack and the reference model.
- computing popularity drift (pd) by comparing technology adoption against community benchmarks.
- calculating performance drift (perfd) by measuring deviations from reference performance metrics.
- evaluating compatibility drift (cd) based on ease of integration with modern technologies.
- normalizing drift scores to ensure consistency across components
- outputting drift scores for all technology stack components.
D. scoring module (240)
- inputting technological stack
- generating LLM reference model
- performing drift analysis(statistical method)
- applying scoring formula
- outputting final score (1-100)
E. output generation module (250).

Dated this 30th day of December, 2024.