Contextual Product Feature Modeling From Requirements Documents
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Solution Overview
Problem
Conventional methods for understanding product features in complex domains require manual search and maintenance across multiple requirement specification documents, leading to inefficiencies and inconsistencies due to duplication and the need for skilled domain experts.
Innovation Solution
A method and system using NLP to automatically extract a contextual product feature model from requirement specification documents, involving sub-product identification, hierarchical feature generation, and contextual modeling based on predefined patterns and dictionaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search and maintenance methods are used across multiple requirement specification documents, then domain experts can understand product functionality and features, but the process becomes cumbersome, error-prone, and time-consuming
Solution Approach 1:
The patent replaces manual mechanical search and analysis processes with an automated NLP-based system. The system uses machine learning models to automatically extract product features, requirements, and relationships from requirement specification documents, eliminating the need for manual domain expert review and significantly reducing time consumption while maintaining accuracy.
Solution Approach 2:
The system enables self-service by automatically processing requirement specification documents without human intervention. The NLP model autonomously identifies product features, extracts requirements, establishes relationships between elements, and generates structured outputs, allowing the system to serve itself in performing tasks that previously required manual domain expert analysis.
2Loss of information
If multiple requirement specification documents are maintained manually, then comprehensive product information is captured, but consistency of information across documents becomes difficult to maintain due to duplication
Solution Approach 1:
The patent merges information from multiple requirement specification documents into a unified structured representation. The NLP system processes all documents simultaneously, extracts relevant information, and integrates it into a consistent product feature model with defined relationships, eliminating duplication and ensuring information consistency across the entire document set.
Solution Approach 2:
The system creates a universal product feature model that serves multiple purposes: it captures comprehensive product information from various documents, maintains consistency across all requirements, and provides a single source of truth for different engineering tasks. This multi-functional model replaces the need to maintain multiple separate document versions.
3Measurement precision
If skilled domain experts manually analyze requirement specification documents, then accurate understanding of product functionality and dependencies is achieved, but the process is cumbersome and requires highly skilled personnel
Solution Approach 1:
The patent replaces the mechanical process of manual domain expert analysis with an automated NLP-based system. The machine learning model has been trained to understand domain-specific terminology and relationships, achieving accurate requirement analysis without requiring human domain experts to manually review documents, thereby reducing the skill level barrier while maintaining analysis accuracy.
Solution Approach 2:
The NLP system acts as an intermediary between requirement specification documents and the final product understanding. It bridges the gap by automatically interpreting complex domain language, extracting meaningful information, and transforming unstructured text into structured product feature models, eliminating the need for skilled experts to perform manual interpretation.
4Loss of information
If comprehensive requirement specification documentation is maintained, then complete product knowledge is available, but manual searching and processing of thousands of pages is inefficient
Solution Approach 1:
The patent extracts essential product feature information and requirements from comprehensive requirement specification documents using NLP techniques. The system identifies and extracts key entities, relationships, and dependencies, pulling out only the critical information needed for requirements engineering tasks while discarding redundant text, thereby maintaining completeness while improving efficiency.
Solution Approach 2:
The system creates structured copies of product knowledge from unstructured requirement documents. Instead of manually searching through original thousands of pages, the NLP system generates replicated structured representations (product feature models, requirement graphs) that contain the same essential information in an easily queryable and processable format, dramatically improving productivity.
Data Source
AI summary
The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model. Various types of feature exports can be generated using a natural language interface.