AI Requirement Generation via NLP and Historical Data
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Solution Overview
Problem
Current methods for creating project requirement documents are often incomplete, ambiguous, and time-consuming, leading to project failures due to ineffective and inconsistent requirements, which can result in schedule delays, cost overruns, and scope creep.
Innovation Solution
The use of machine learning and natural language processing to analyze historical project data, tag requirements by project type, and generate accurate requirement documents for new projects based on domain, technology, and industry considerations, ensuring completeness and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to create requirement documents, then flexibility and customization are improved, but completeness and consistency deteriorate
Solution Approach 1:
The patent introduces an AI-based natural language processing intermediary that mediates between project descriptions and requirement documents. This intermediary automatically extracts project type, technology stack, and domain information, then retrieves and assembles relevant requirements from a database, ensuring both customization through AI understanding and consistency through systematic retrieval of pre-defined requirements.
Solution Approach 2:
The system creates requirement documents by copying and adapting proven requirements from historical projects. The AI identifies similar projects in the database and replicates their requirement structures, ensuring consistency and completeness while allowing customization through AI-based analysis of the current project's specific needs.
2Reliability
If comprehensive requirement analysis is performed manually, then completeness is improved, but time consumption deteriorates
Solution Approach 1:
The system performs preliminary action by pre-tagging and organizing requirements in the database according to project type, technology stack, and domain categories during historical project analysis. When a new project requires requirements, the AI quickly retrieves pre-organized requirements matching the project characteristics, ensuring completeness without time-consuming manual analysis.
Solution Approach 2:
The patent replaces the mechanical manual analysis system with an AI-based automated system. The natural language processing and machine learning algorithms automatically analyze project descriptions, identify relevant requirements, and generate comprehensive requirement documents, achieving both completeness and time efficiency that manual processes cannot provide.
3Measurement precision
If historical project data is extensively analyzed, then accuracy of requirement matching is improved, but processing complexity deteriorates
Solution Approach 1:
The patent segments the complex task of requirement matching into distinct components: extracting project type, identifying technology stack, determining domain category, and retrieving corresponding requirements. The AI processes each segment separately using specialized natural language processing techniques, achieving high accuracy while managing complexity through modular processing.
Solution Approach 2:
The system changes parameters by transforming unstructured project descriptions into structured data with defined parameters such as project type, technology stack, and domain. This parameterization enables precise matching with stored requirements while simplifying the processing complexity through standardized data formats and classification schemes.
Data Source
AI summary
A computer implemented method of creating a requirement document that may begin with receiving data feeds for requirement data correlated to historical projects; and using natural language processing to extract requirement data correlated to project type from the historical projects. The project types are tagged with the requirement data and stored in a database of tagged requirements to project type. A project description is analyzed for project type using natural language processing to extract the project type. The method further includes matching the project type extracted from the project description to the tagged requirements of project type. A report is generated of requirements including the tagged requirements matching the project type extracted from the project description.