Description:FIELD OF INVENTION:
The present invention relates to software deployment. More specifically, it relates to a system and method for automatically defining post-deployment success metrics using generative and deterministic analysis of release artifacts and operational signals.

BACKGROUND OF THE INVENTION:
Conventional post-deployment success metrics are the specific data point or key performance indicators, which act as specific quantifiable data points use to evaluate the performance and success of software project after its deployment. Additionally, post-deployment monitoring system are crucial for measuring post-deployment success metrics by providing real-time data on system performance, user experience, and potential issues, enabling proactive adjustments and ensuring long-term stability and user satisfaction. Traditional post-deployment monitoring systems rely on manually defined metrics and alerts that fail to adapt to the specific features or use cases of a given release. However, challenges include data overload, integrating diverse systems, and ensuring timely and actionable insights.
Prior arts:
US11586181B2 discloses system and method for adjusting process parameters in a production environment; wherein the system collects data from multiple input channels in a production environment, analyses process parameters to detect conditions, and adjusts operational processes accordingly whereas our invention provides a system that ingests release management artifacts (e.g., Jira, Aha), code repositories, technical documentation, logs, and customer support data. Our invention uses LLMs for feature identification and success metric generation, while the present invention employs circuits for process condition detection and response.
US11403125B2 discloses system for optimizing the deployment of virtual resources and automating post-deployment actions in a cloud environment; which monitors the performance of deployed virtual machines (VMs), analyses their configurations, and determines optimal deployment options for new VMs based on collected data.
US11580422B1 discloses the system and method for data processing and enterprise AI applications , that validates medical machine learning models by comparing them to reference models and monitoring data anomalies; whereby the system include concentrators to receive and forward time-series data from sensors or smart devices, include message decoders to receive messages comprising the time-series data and storing the messages on message queues, a persistence component to store the time-series data in a key-value store and store the relational data in a relational database; whereas the present invention use dynamic post-deployment monitoring to assess performance. They rely on adaptive benchmarks and multi-source feedback.
Though the prior arts talk about the automated optimization of the post-deployment virtual systems, the prior arts fail to provide an all-in-one solution that integrates semantic extraction, graph-based linkage, historical learning, and LLMs for success metric generation; thereby providing intelligent, dynamic adaptation to each release’s objective, rather than relying on static metrics. Evidently, there is a need for an intelligent system that can dynamically determine success criteria based on the objectives and content of a release, its actual usage, customer feedback, and system behaviour post-deployment.
The present invention overcomes the aforementioned drawbacks by providing an improved system and method for automatically defining and configuring data points, which are used to accurately measure, monitor, and analyse the performance and stability of software applications across various operational environments. Because the system and method are fully automated, the need for manual intervention is completely eliminated, thereby reducing the potential for human error and significantly enhancing the accuracy, consistency, and efficiency of performance measurements.

DEFINITIONS:
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but not limited to, system for automatically defining post-deployment success metrics with input and output devices, processing unit, plurality of mobile devices, a mobile device-based application. It is extended to computing systems like mobile phones, laptops, computers, PCs, and other digital computing devices.
The term “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 term “processing unit” refers to the computational hardware or software that performs the database analysis, generation of graphs, detection of dead code, processing, removal of dead code, and like. It includes servers, CPUs, GPUs, or cloud-based systems that handle intensive computations.
The term “output unit” used hereinafter in this specification refers to hardware or digital tools that present processed information to users including, but not limited to computer monitors, mobile screens, printers, or online dashboards.
The term “Large Language Models” or “LLMs” used hereinafter in this specification refers to systems that use natural language understanding to interpret and generate text. In this system, they help extract features and suggest metrics.
The term “semantic analysis” used hereinafter in this specification refers to the process of interpreting the meaning of data (e.g., stories, commits) to identify relevant software elements.
The term “knowledge graph” used hereinafter in this specification refers to a structured representation of concepts (nodes) and their relationships (edges), such as linking a feature to metrics, logs, or customer tickets.
The term “regression detection” used hereinafter in this specification refers to finding degradations in system performance post-release compared to pre-release baselines.
The term “KPI” or “Key Performance Indicator” used hereinafter in this specification refers to a measurable value that shows how effectively a feature or system is performing.
The term “anomalies” used hereinafter in this specification refers to unexpected or abnormal patterns in system logs that may indicate issues.
The term “telemetry” used hereinafter in this specification refers to an automated process of collecting and transmitting data from remote sources for monitoring and analysis, enabling effective management and control of systems.

OBJECTS OF THE INVENTION:
The primary object of the present invention is to provide a system and method for automatically defining post-deployment success metrics.
Another object of the invention is to provide a system and method that uses generative and deterministic analysis of release artifacts and operational signals.
Yet another object of the present invention is to provide a system and method which applies generative models (LLMs) and deterministic rule-based methods.
Yet another object of the present invention is to provide a system and method that provides intelligent, dynamic adaptation to each release’s objective, rather than relying on static metrics.
Yet another object of the present invention is to provide a system and method that bridges engineering and customer domains.
Yet a further object of the present invention is to provide a system and method that enables proactive regression detection and success prediction without manual intervention.

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 automatically defining post-deployment success metrics using generative and deterministic analysis of release artifacts and operational signals; wherein the system works due to interaction of user, one or more input and output devices and processing unit comprising of data sources, a data ingestion engine, a semantic extractor, a graph constructor, a regression engine, a weighing engine and a report generator.
The components interact to employ a method comprising the steps of ingesting data from various data sources, using LLMs and deterministic rules to extract and identify features, mapping known patterns to metrics and flags anomalies; constructing graphs, analyzing pre- and post-deployment telemetry to flag behavioral changes, learning from historical release data, and generating a report configured to output prioritized success metrics.
The system and method enables a context-aware metric generation, combines generative-AI with rule-based validation for high precision, learns from historical data to improve over time, connects operational metrics to customer experience automatically and provides a graph-based analysis that improves root cause and dependency tracking, provides an intelligent, dynamic adaptation, removes dependency on static metrics, bridges engineering and customer domains, enables proactive regression detection and success prediction without manual intervention.

BRIEF DESCRIPTION OF THE DRAWINGS:
A complete understanding of the present invention may be made by reference to the following detailed description 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 the components of the system.
Fig. 2. illustrates the stepwise method employed by the present system.

DETAILED DESCRIPTION OF THE 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.
The present invention discloses a system and method for automatically defining post-deployment success metrics using generative and deterministic analysis of release artifacts and operational signals; wherein the system (100) works due to interaction of user, one or more input and output devices and processing unit comprising of data sources (110), a data ingestion engine (120), a semantic extractor (130), a graph constructor (140), a regression engine (150) a weighing engine (160) and a report generator (170).
In an embodiment of the invention, the data ingestion engine (120) is configured to retrieve structured and unstructured inputs such as data epics, stories, commits, PRs, logs or tickets from various data sources (110) including, but not limited to release management, source control, documentation, and customer support systems using project management tools; for example Jira, Aha, GitHub, Confluence or ServiceNow.
In a next embodiment of the invention, the semantic extractor (130) uses a combination of large language models (LLMs) and deterministic rules to extract and identify features, use cases, architectural components, and key performance indicators associated with a release; wherein the semantic extractor (130) enables rule engine uses a common pattern and architecture list to map known patterns to metrics so as to validate the drift and to check and flag the anomalies.; and uses LLMs to generate best practices and ideal success metrics for extracted use cases.
In a next embodiment of the invention, the graph constructor (140) is used to model features, metrics, customer feedback, and logs as interconnected nodes and relationships; wherein the graph constructor (140) construct graphs comprising of nodes and edges; where nodes represent feature, UseCase, Log, CustomerTicket, Metric, Release, Service; and the edges represent relationships including, but not limited to DEPENDS_ON, IMPACTS, GENERATES, ASSOCIATED_WITH.
In a next embodiment of the invention, the regression detection engine (150) analyzes pre- and post-deployment telemetry to flag behavioral changes; such that the regression detection engine (150) correlates log anomalies and customer support tickets to features via the knowledge graph; and the weighting engine (160) adjusts feature weights dynamically based on post-deployment usage patterns, system health indicators, and customer sentiment; where the engine (160) learns from historical release data to assign relative importance to each feature or use case; track metrics over time to detect regression and usage changes; and integrate log analysis and customer ticket volume.
In a next embodiment of the invention, the report generator (170) is configured to output prioritized success metrics tailored to the specific content and impact of the release; where the report generator (170) formats outputs as both machine-readable (e.g., JSON) and human-readable (e.g., PDF or UI widget) representations presented using at least one output device such as dashboard; wherein the output includes the multiple sections representing summary, feature-use case map, regression highlights, key metrics, success criteria.
In a preferred embodiment of the invention, the system through interaction of various components, employs a method for automatically defining post-deployment success metrics; comprising the steps as follows:
Step 1: Data ingestion:
The system retrieves structured and unstructured inputs such as data epics, stories, commits, PRs, logs or tickets from various data sources (110) using various project management tools. Data ingestion includes ETL (extract, transform, load), webhooks to automatically send real-time data to another system as events happen; or metadata extraction.
Step 2: Semantic discovery:
The semantic extractor (130) uses a combination of large language models (LLMs) and deterministic rules to extract and identify features, use cases, architectural components, and key performance indicators associated with a release; enables rule engine to map known patterns to metrics and flags anomalies; and generate best practices and ideal success metrics for extracted use cases.
Step 3: Graph Construction:
The graph constructor (140) construct graphs by interconnecting nodes and edges; where “nodes” represent feature, UseCase, Log, CustomerTicket, Metric, Release, Service; and the “edges” represent relationships including, but not limited to DEPENDS_ON, IMPACTS, GENERATES, ASSOCIATED_WITH.
Step 4: Regression analysis:
The regression detection engine (150) analyzes pre- and post-deployment telemetry to flag behavioral changes; correlates log anomalies and customer support tickets to features via the knowledge graph.
Step 5: Weighting analysis:
The weighting engine (160) learns from historical release data to assign relative importance to each feature or use case; track metrics over time to detect regression and usage changes; and integrate log analysis and customer ticket volume.
Step 6: Report generation:
The report generator (170) is configured to output prioritized success metrics tailored to the specific content and impact of the release; where the report generator (170) formats outputs as both machine-readable (e.g., JSON) and human-readable (e.g., PDF or UI widget) representations
In a next embodiment of the invention, the system and method of the present invention enables a context-aware metric generation based on actual release objectives, combines generative-AI with rule-based validation for high precision, learns from historical data to improve over time, connects operational metrics to customer experience automatically and provides a graph-based analysis that improves root cause and dependency tracking, provides an intelligent, dynamic adaptation to each release’s objective thereby removing the dependency on static metrics, bridges engineering (code/logs) and customer domains (tickets, feedback), enables proactive regression detection and success prediction without manual intervention.
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 automatically defining post-deployment success metrics using generative and deterministic analysis of release artifacts and operational signals; wherein the system (100) works due to interaction of user, one or more input and output devices and processing unit comprising of data sources (110), a data ingestion engine (120), a semantic extractor (130), a graph constructor (140), a regression engine (150), a weighing engine (160) and a report generator (170);

characterized in that:
the system through interaction of various components employs a method for automatically defining post-deployment success metrics; comprising the steps of;
- ingesting data by the data ingestion engine (120); thereby retrieving structured and unstructured inputs from various data sources (110) using various project management tools;
- using a combination of large language models (LLMs) and deterministic rules by the semantic extractor (130) to extract and identify features, use cases, architectural components, and key performance indicators associated with a release;
- enabling the rule engine by the semantic extractor (130) to map known patterns to metrics and flags anomalies; and generate best practices and ideal success metrics for extracted use cases;
- constructing graphs by interconnecting nodes and edges by the graph constructor (140); where “nodes” represent feature, and the “edges” represent relationships;
- analyzing pre- and post-deployment telemetry to flag behavioral changes; correlating log anomalies and customer support tickets to features by the regression detection engine (150);
- learning from historical release data to assign relative importance to each feature; track metrics over time to detect regression and usage changes, integrate log analysis and customer ticket volume by the weighting engine (160);
- generating a report configured to output prioritized success metrics tailored to the specific content and impact of the release by the report generator (170).

2. The system and method as claimed in claim 1, wherein the input includes data epics, stories, commits, PRs, logs or tickets; and data ingestion includes ETL (extract, transform, load), webhooks or metadata extraction.

3. The system and method as claimed in claim 1, where “nodes” represent feature including UseCase, Log, CustomerTicket, Metric, Release, Service; and “edges” represent relationships including DEPENDS_ON, IMPACTS, GENERATES, ASSOCIATED_WITH.

4. The system and method as claimed in claim 1, wherein the report generator (170) formats outputs as both machine-readable (e.g., JSON) and human-readable (e.g., PDF or UI widget) representations, using an output unit such as dashboard.

5. The system and method as claimed in claim 1, wherein the system enables a context-aware metric generation based on actual release objectives, combines generative-AI with rule-based validation for high precision, learns from historical data to improve over time, connects operational metrics to customer experience automatically and provides a graph-based analysis that improves root cause and dependency tracking, provides an intelligent, dynamic adaptation to each release’s objective thereby removing the dependency on static metrics, bridges engineering (code/logs) and customer domains (tickets, feedback), enables proactive regression detection and success prediction without manual intervention.

Dated this 11th day of April, 2025.