Description:FIELD OF INVENTION:
The invention relates to the field of software development and product lifecycle management, and more specifically to system and method for data-driven prioritization of user stories using multisource graph-enhanced scoring and insight generation; thereby providing systems for prioritizing user stories in a product backlog by leveraging customer data, support interactions, and market trends.

BACKGROUND:
Product lifecycle management is a system that manages the entire lifecycle of a product, from initial ideation to retirement, thereby ensuring systematic coordination, enhanced product quality, and reduced time-to-market. Central to this framework is a prioritization system, which constitutes a structured mechanism for evaluating and ranking tasks, projects, or objectives based on predetermined criteria. A prioritization system enables individuals and teams to make informed and strategic decisions regarding the order of execution of tasks. In furtherance of this objective, product lifecycle management processes employ a product backlog as the primary tool for prioritization. A product backlog is a dynamic and prioritized compilation of all proposed features, functionalities, enhancements, technical requirements, and issue resolutions. The product backlog operates as a unified and authoritative reference for the development team, and continually evolves in response to emerging data and shifting priorities.
Through a process of ongoing refinement, the product backlog items are subdivided into actionable tasks, supplemented with necessary details, and aligned with implementation objectives. The product backlog contains various types of items, including user stories which capture the “who,” “what,” and “why” of a user requirement, thereby facilitating value creation for the end-users.
However, traditional prioritization methods remain constrained by manual processes, subjectivity, and incomplete information. These limitations impair the development team’s ability to consistently deliver features that align with user needs and business goals, thereby resulting in inefficiencies within product lifecycle management.
To overcome these drawbacks, there is a need for an integrated, scalable, and explainable prioritization system that will leverage the available opportunities created by intelligent systems and data availability, automate processes with minimal manual intervention, and ultimately enhance the delivery of customer-centric features while optimizing engineering resources.

Prior Arts:
US20140280117A1 discloses a system that prioritizes items based on user activity. Specifically, it determines user interest from current user activity and subsequently prioritizes items in a list displayed to the user based on the inferred interest.
US8862680B2 introduces a method and system for data prioritization. The disclosed system assigns urgency and importance to data items and prioritizes them accordingly. In a particular embodiment, the system utilizes such prioritization for data communication purposes, including dispatching the data.
US9069864B2 relates to methods for prioritizing content items for a user. In one aspect, a method includes receiving user authentication events corresponding to a user account, determining a time distribution of the received user authentication events, constructing a content prioritization user model corresponding to the user associated with the user account, receiving a content item associated with the user, associating a content priority value with the content item which corresponds to a predicted aspect of the user.
While the aforementioned prior arts provide systems for prioritization systems, they remain limited in their scope. None of these systems address the need for a comprehensive, transparent, and explainable prioritization framework which could integrate multiple data sources into a unified prioritization mechanism. Thus, there exists a need for an integrated, scalable, and explainable prioritization system that will leverage the available opportunities created by intelligent systems and data availability, automate processes with minimal manual intervention, and ultimately enhance the delivery of customer-centric features while optimizing engineering resources.

DEFINITIONS:
The expression “system” used hereinafter in this specification refers to an ecosystem comprising, but not limited to, system for data-driven prioritization of user stories 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 “user story” used hereinafter in this specification refers to a short, simple description of a feature told from the perspective of the user or customer, used in Agile software development.
The term “embedding-based semantic matching” used hereinafter in this specification refers to a technique where texts (like user stories or trends) are converted into numerical vectors, and similarity is calculated (e.g., cosine similarity).
The term “Cosine Similarity” used hereinafter in this specification refers to a metric used to measure how similar two vectors are, commonly used in natural language processing.
The term “CRM (Customer Relationship Management)” used hereinafter in this specification refers to systems like Salesforce or Hubspot that manage a company’s interactions with current and potential customers.
The term “ETL (Extract, Transform, Load) ” used hereinafter in this specification refers to a data integration process used to extract data from various sources, transform it into a suitable format, and load it into a target system.
The term “effort penalty” used hereinafter in this specification refers to a deduction from the priority score based on the estimated development effort (story points or historical data).
The term “LLM (Large Language Model)” used hereinafter in this specification refers to advanced AI models (e.g., GPT, Qwen) that generate human-like text, used here for generating justifications and classifying stories.
The term “feedback loop / reinforcement learning” used hereinafter in this specification refers to: A mechanism where sprint outcome data is fed back into the system to improve future prioritization through adaptive learning.
The term “REST API (Representational State Transfer)” used hereinafter in this specification refers to a standard interface for web services that allows integration between systems (e.g., Jira, Aha).

OBJECTS OF THE INVENTION:
The primary object of the invention is to provide a system and method for data-driven prioritization of user stories.
Another object of the invention is to provide a system and method that uses multisource graph-enhanced scoring and insight generation.
Yet another object of the invention is to provide a system and method for prioritizing user stories in a product backlog by leveraging customer data, support interactions, and market trends.
Yet another object of the invention is to provide LLM augmented justifications with minimum human intervention thereby increasing trust and transparency of prioritization.
Yet another object of the invention is to provide semantic market fit scoring by using embedding-based alignment between product backlog and real-world market trends.
Yet another object of the invention is to provide self-improving prioritization loop, enabling feedback-driven continuous learning through sprint impact data.

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 invention relates to the field of software development and product lifecycle management, and more specifically to system and method for data-driven prioritization of user stories using multisource graph-enhanced scoring and insight generation; thereby providing systems for prioritizing user stories in a product backlog by leveraging customer data, support interactions, and market trends; comprises of input unit, processing unit, output unit and end users; wherein the processing unit comprises of plurality of modules such as a data ingestion layer, a feature engineering and graph construction layer, a scoring and prioritization engine, an insight generation layer, prioritization orchestration engine, feedback loop engine and a visualization and API Layer.
In another aspect of the invention, the method includes steps of- ingesting data from data sources, normalizing, deduplicating, and cleaning data stream; constructing a property graph computing a composite score, executing prioritization logic, combining scores, classifications, feedback, providing sprint outcome data and displaying prioritized backlogs, graphs, and justification chain.
In yet another aspect, the system and method of the present invention provides a multi-source graph architecture using metadata into a unified graph prioritization; provides LLM augmented justifications generates rationales per user story with minimum human intervention thereby increasing trust and transparency of prioritization; provides semantic market fit scoring by using embedding-based alignment between product backlog and real-world market trends; and provides self-improving prioritization loop enabling feedback-driven continuous learning.

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 escribeed 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 data-driven prioritization of user stories using multisource graph-enhanced scoring and AI-augmented insight generation; thereby providing automated and intelligent systems for prioritizing user stories in a product backlog by leveraging customer data, support interactions, and market trends. The system (100) comprises of input unit (10), processing unit (20), output unit (30) and end users (40); wherein the processing unit comprises of plurality of modules such as a data ingestion layer (21), a feature engineering and graph construction layer (22), a scoring and prioritization engine (23), an insight generation layer (24), prioritization orchestration engine (25), feedback loop engine (26) and a visualization and API Layer (27).
In an embodiment of the invention, the input unit (10) comprises of heterogenous data sources including systems such as CRM systems (11) configured to store renewal dates, ARR or pipeline stage (e.g. Salesforce or Hubspot); support ticketing systems (12) which store ticket severity, volume, sentiment, topics (e.g. Zendesk, Freshdesk); market intelligence (13) that allow trend data and competitive analysis (e.g. RSS, Reddit, Gartner, Competitor sites); and product management tools (14) for storing backlog of user stories and epics (e.g. Jira, Aha); and the end users (40) include, but are not limited to product managers (41), engineering teams (42) or executives (43).
In a next embodiment of the invention, the data ingestion layer (21) ingests data from the heterogenous data sources such as CRM systems (11), support ticketing systems (12), market intelligence (13) and product management tools (14); where each data stream is normalized, deduplicated, and cleaned using custom ETL pipelines orchestrated by data ingestion layer (21) using an open-source tool to programmatically author, schedule, and monitor workflows (e.g. Apache airflow); or a distributed platform used for building real-time streaming data pipelines (e.g. Apache Kafta).
In a next embodiment of the invention, the feature engineering and graph construction layer (22) constructs a property graph having nodes that represent features from CRM (11), support (12) and trends (13) such as UserStory, Account, Trend, SupportCluster; and edges that represent relationships such as REQUESTED_BY, MATCHES_TREND, RELATED_TO, IMPACTS between the nodes; wherein the unique graph-based structure allows querying and traversal-based reasoning, enabling explainability and clustering the features. It is to be noted that each user story is enriched with features as exemplified below:
• CRM: renewal_in_days, ARR_at_risk, segment_tier
• Support: ticket_count, avg_severity, pain_index
• Trends: alignment_score, urgency_signal
In a next embodiment of the invention, the scoring and prioritization engine (23) is configured to compute a composite score for each user story using a hybrid approach which includes:
• Weighted Scoring Model (WSM); where scores are calculated based on the formula:
P = w1∗RevenueImpact + w2∗SupportPain + w3∗TrendFit + w4∗EffortPenalty ; where weights are customizable by product managers,
Revenue impact is the gain/loss of revenue with-respect-to the addition/removal of feature or capability. In the context of customer support, a "support pain index" is a metric that gauges the overall level of customer dissatisfaction or frustration related to support interactions. Trend fit in context to technical trend is a combination of alignment of that trend to the product or domain, and urgency in context to customer or business demands. Effort Penalty is the cost of making/building the feature, in addition to cost and impact due to prioritizing this over other features/capabilities.
• Effort Adjustment; that applies an effort penalty based on user story point estimates or historical delivery data to normalize data for implementation feasibility,
• Kano Model Classification (a theory of product development and customer satisfaction); which is an LLM-based classifier that tags each story as Must-Have, Performance, or Delighter such that a lookup table adjusts scoring bonuses accordingly. These are type of stories - like “must-have” means something that the product must have (in context to market, business, or competition). “Performance” is like scalability related and “delighter” is like something that is nice to have and can get a “wow” from customers.
• Rule-Based Overrides wherein the deterministic triggers boost priority by fixed factors (e.g., ARR (annual recurring revenue) > $1M and renewal < 30 days).
In yet another embodiment of the invention, insight generation layer (24) is configured with AI (artificial intelligence) and ML (machine learning) which uses a combination of:
• Topic Modelling; to discover clusters of customer pain across tickets, and match them to stories using embedding-based similarity (e.g. e5-small-v2, OpenAI or HuggingFace models, BERTopic, LDA);
• Supervised Models; which are trained on historical story priorities and outcomes (e.g., impact on churn or CSAT), using features such as, but not limited to account size, effort, ticket themes.
• Semantic Trend Matching; wherein the user stories are vectorized and compared to trend vectors using cosine similarity such that the threshold-based alignment scoring determines story-to-trend fit.
• LLM-Generated Justifications; wherein LLMs (e.g. GPT data or Qwen models) generate narrative explanations for each story’s score, linking data sources to a human-readable rationale.
In yet another embodiment of the invention, the prioritization orchestration engine (25) executes the prioritization logic, combining scores, classifications, and feedback inputs; wherein the engine (25) produces a final ranked backlog, a user story metadata with scores, tags, and justifications, and segment-based views including parameters such as region, tier, or strategic theme; such that the engine (25) enables simulations and conditional (what-if) analysis through adjustable weight sliders and filters. The adjustable weight sliders are adjustable controls (typically UI sliders) used to assign relative importance (weights) to different parameters or inputs in the prioritization logic. They allow users (like product managers or business analysts) to simulate different prioritization scenarios by increasing or decreasing the influence of specific factors.
Example: A slider for Customer Impact could be set higher if the team wants to prioritize user-facing issues. Another for Revenue Potential could be increased to focus on monetizable features. Adjusting sliders changes how the final scores of user stories are calculated—impacting their position in the ranked backlog. Filters are selection criteria that help narrow down the view of the backlog or story list based on certain metadata or dimensions. The purpose is to provide segment-based views, allowing stakeholders to focus only on relevant subsets of stories. Some common Filters are Region: Show only stories relevant to APAC or North America; Tier: Focus on high-priority customers or internal tiers; Strategic Theme: Filter stories aligned with specific OKRs or business goals.

These filters do not change scores but refine what is displayed or analyzed in simulations and views
In yet another embodiment of the invention, the feedback loop engine (26) provides a sprint outcome data that includes, but is not limited to delivered stories, customer impact and feedback, which are collected post-sprint; whereafter the system uses this data to adjust model weights using reinforcement learning principles, re-train classifiers based on story impact or fine-tune effort estimates using velocity data. The feedback loop engine (26) plays a crucial role in adaptive learning and continuous optimization of the prioritization and estimation models by leveraging reinforcement learning (RL) principles. Post-Sprint Data Collection (The Reward Signal). After each sprint, the engine (26) gathers a comprehensive set of outcome data, which includes:
• Delivered stories (which ones were completed)
• Customer feedback and satisfaction metrics (qualitative and quantitative impact)
• Story-level business value realization
• Deviation from initial estimates (time, effort)
• Team velocity and throughput
This post-sprint data serves as the reward signal in RL—a reflection of how well the system's past decisions (e.g., prioritization, effort estimation) aligned with actual outcomes. The system then uses this data to iteratively improve its internal models through RL-like mechanisms, enabling the system to learn optimal strategies over time.
Model Weight Adjustment: Based on the difference between expected and actual outcomes, the engine adjusts the internal scoring weights. For example, if "Customer Impact" consistently correlates with higher satisfaction scores, its weight is reinforced. This mimics the policy update in RL, where actions leading to better outcomes are favoured in the future.
Classifier Re-training Based on Story Impact: The system re-trains its classification models (e.g., classifying a story as strategic vs. tactical) using labelled data from sprint outcomes. Stories that performed well (based on impact or feedback) become positive examples for model refinement. This is similar to reward shaping or experience replay in RL, where the model learns from specific successful or failed episodes.

Effort Estimation Fine-Tuning via Velocity: The velocity data (e.g., story points completed per sprint) is used to fine-tune the effort estimation model. If effort predictions were consistently optimistic or pessimistic, the system adapts to become more accurate. This aligns with the RL principle of updating value functions to better predict outcomes of future actions (e.g., how much effort is truly required).
In yet a further embodiment of the invention, the visualization and API layer (27) displays prioritized backlogs, graphs, and justification chain using an output unit (30) such as a frontend dashboard built using libraries (e.g. React/ Streamlit/ REST APIs) that expose endpoints for integration with centralized systems for detecting issues or bugs (e.g. Jira, Aha)
In a preferred embodiment of the invention, the system employs a method for data-driven prioritization of user stories comprising the steps of:
• ingesting data from the heterogenous data sources including CRM systems (11), support ticketing systems (12), market intelligence (13) and product management tools (14) by data ingestion layer (21);
• normalizing, deduplicating, and cleaning the data stream using custom ETL pipelines orchestrated by data ingestion layer (21);
• allowing querying and traversal-based reasoning, enabling explainability and clustering the features; and constructing a property graph having nodes that represent features and edges that represent relationships, using feature engineering and graph construction layer (22);
• computing a composite score for each user story by the scoring and prioritization engine (23), using a hybrid approach comprising of weighted scoring model (WSM), effort adjustment, kano model classification, and rule-based overrides;
• enabling the insight generation layer (24) to use a combination of topic modelling, supervised models, semantic trend matching, and LLM-generated justifications;
• executing the prioritization logic, combining scores, classifications, and feedback inputs by the prioritization orchestration engine (25); thereby producing a final ranked backlog, a user story metadata with scores, tags, and justifications, and segment-based views;
• enabling simulations and conditional (what-if) analysis through adjustable weight sliders and filters by the prioritization orchestration engine (25);
• providing a sprint outcome data including delivered stories, customer impact and feedback, collected post-sprint by the feedback loop engine (26); thereby allowing the system to use this data to adjust model weights;
• displaying prioritized backlogs, graphs, and justification chain by the visualization and API layer (27) using an output unit (30) like a frontend dashboard.
According to an embodiment of the invention, the present system and method provides significant advantages such as:
- providing a multi-source graph architecture wherein the system uses CRM systems (11), support ticketing systems (12), market intelligence (13) and product management tools (14) metadata into a unified graph prioritization;
- providing LLM augmented justifications generates rationales per user story with minimum human intervention thereby increasing trust and transparency of prioritization;
- providing semantic market fit scoring by using embedding-based alignment between product backlog and real-world market trends;
- providing self-improving prioritization loop thereby enabling feedback-driven continuous learning through sprint impact data.
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. , C , C , Claims:We claim,
1. A system and method for data-driven prioritization of user stories using multisource graph-enhanced scoring and ai-augmented insight generation;

wherein the system (100) comprises of input unit (10), processing unit (20), output unit (30) and end users (40); wherein the processing unit (30) comprises of plurality of modules such as a data ingestion layer (21), a feature engineering and graph construction layer (22), a scoring and prioritization Engine (23), an insight generation layer (24), prioritization orchestration engine (25), feedback loop engine (26) and a visualization and API Layer (27);

characterized in that:
the system employs a method for data-driven prioritization of user stories comprising the steps of;
• ingesting data from the heterogenous data sources including CRM systems (11), support ticketing systems (12), market intelligence (13) and product management tools (14) by data ingestion layer (21);
• normalizing, deduplicating, and cleaning the data stream using custom ETL pipelines orchestrated by data ingestion layer (21);
• allowing querying and traversal-based reasoning, enabling explainability and clustering the features; and constructing a property graph having nodes that represent features and edges that represent relationships, using feature engineering and graph construction layer (22);
• computing a composite score for each user story by the scoring and prioritization engine (23), using a hybrid approach comprising of weighted scoring model (WSM), effort adjustment, kano model classification, and rule-based overrides;
• enabling the insight generation layer (24) to use a combination of topic modelling, supervised models, semantic trend matching, and LLM-generated justifications;
• executing the prioritization logic, combining scores, classifications, and feedback inputs by the prioritization orchestration engine (25); thereby producing a final ranked backlog, a user story metadata with scores, tags, and justifications, and segment-based views;
• enabling simulations and conditional (what-if) analysis through adjustable weight sliders and filters by the prioritization orchestration engine (25);
• providing a sprint outcome data including delivered stories, customer impact and feedback, collected post-sprint by the feedback loop engine (26); thereby allowing the system to use this data to adjust model weights;
• displaying prioritized backlogs, graphs, and justification chain by the visualization and API layer (27) using an output unit (30) like a frontend dashboard.

2. The system and method as claimed in claim 1, wherein the input unit (10) comprises of heterogenous data sources including CRM systems (11) configured to store renewal dates, ARR or pipeline stage, support ticketing systems (12) which store ticket severity, volume, sentiment, topics, market intelligence (13) that allow trend data and competitive analysis, and product management tools (14) for storing backlog of user stories and epics; and the end users (40) include product managers (41), engineering teams (42) or executives (43).

3. The system and method as claimed in claim 1, wherein the “nodes” represent features from CRM (11), support (12) and trends (13) such as UserStory, Account, Trend, SupportCluster; and “edges” represent relationships such as REQUESTED_BY, MATCHES_TREND, RELATED_TO, IMPACTS between the nodes.

4. The system and method as claimed in claim 1, wherein the scoring and prioritization engine (23) uses weighted scoring model (WSM), where scores are calculated based on the formula: P = w1∗RevenueImpact + w2∗SupportPain + w3∗TrendFit + w4∗EffortPenalty; where weights are customizable by product managers;

effort adjustment, that applies an effort penalty based on user story point estimates or historical delivery data to normalize data for implementation feasibility;
kano model classification, which is an LLM-based classifier that tags each story as Must-Have, Performance, or Delighter such that a lookup table adjusts scoring bonuses accordingly;
rule-based overrides wherein the deterministic triggers boost priority by various fixed factors.

5. The system and method as claimed in claim 1, wherein insight generation layer (24) uses topic modelling to discover clusters of customer pain across tickets, and match them to stories using embedding-based similarity;
supervised models trained on historical story priorities and outcomes using features such as account size, effort, ticket themes;
semantic trend matching, where the user stories are vectorized and compared to trend vectors using cosine similarity such that the threshold-based alignment scoring determines story-to-trend fit; and
LLM-generated justifications; wherein LLMs generate narrative explanations for each story’s score, linking data sources to a human-readable rationale.

6. The system and method as claimed in claim 1, wherein the feedback loop engine (26) uses post-sprint data to adjust model weights using reinforcement learning principles, re-train classifiers based on story impact or fine-tune effort estimates using velocity data.

7. The system and method as claimed in claim 1, that provides significant advantages such as providing a multi-source graph architecture using CRM systems (11), support ticketing systems (12), market intelligence (13) and product management tools (14) metadata into a unified graph prioritization; providing LLM augmented justifications generates rationales per user story with minimum human intervention thereby increasing trust and transparency of prioritization; providing semantic market fit scoring by using embedding-based alignment between product backlog and real-world market trends; and providing self-improving prioritization loop thereby enabling feedback-driven continuous learning through sprint impact data.

8. The system and method as claimed in claim 1, wherein the adjustable weight sliders are adjustable controls such as UI sliders used to assign relative importance or weights to different parameters or inputs in the prioritization logic and they allow users to simulate different prioritization scenarios by increasing or decreasing the influence of specific factors.

9. The system and method as claimed in claim 1, wherein the filters are selection criteria that help narrow down the view of the backlog or story list based on certain metadata or dimensions, in order to provide segment-based views, allowing stakeholders to focus only on relevant subsets of stories and the filters are selected from region, tier and strategic theme; and these filters refine what is displayed or analyzed in simulations and views.