AI Project Manifest System for Dynamic Construction Constraints
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Solution Overview
Problem
Conventional construction and manufacturing project management systems are inefficient in adapting to dynamic factors and fail to provide real-time insights or actionable guidance, leading to cost and schedule overruns, quality degradation, and inefficiencies due to reliance on manual and rule-based approaches that cannot handle complex, varied inputs effectively.
Innovation Solution
An AI-based system that creates an optimized project manifest by receiving project requests, determining objectives and constraints through a knowledge repository and data feeds, applying deep-learning and AI/ML processes to generate an optimized model, and providing real-time updates and recommendations for improved project execution, including cost, schedule, and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional manual and rule-based approaches are used for project management, then system complexity is reduced, but adaptability to dynamic factors and real-time decision-making capability deteriorate
Solution Approach 1:
The patent replaces manual mechanical processes with an AI-based system that uses machine learning models to automatically analyze project data, predict outcomes, and generate recommendations. The system substitutes human decision-making with automated intelligent algorithms that can process complex data patterns and adapt to dynamic project conditions in real-time.
Solution Approach 2:
The system enables self-service by automatically analyzing project data from multiple sources, generating its own insights through machine learning, and providing actionable recommendations without requiring constant human intervention. The AI model continuously learns from project data and autonomously adapts to changing conditions, reducing the need for manual analysis and decision-making.
2Productivity
If AI-based automated systems are implemented, then real-time insights and decision-making capability are improved, but system complexity and implementation cost increase
Solution Approach 1:
The patent segments the AI-based system into distinct functional modules: data collection components, machine learning analysis modules, visualization interfaces, and recommendation generation components. This modular architecture allows the system to handle complex project management tasks by breaking them down into manageable segments that can be independently developed, deployed, and maintained.
Solution Approach 2:
The system introduces an intermediary layer between raw project data and decision-making processes. The machine learning model acts as an intermediary that processes, analyzes, and interprets complex project data, transforming it into actionable insights and recommendations. This intermediary layer simplifies the interaction between data and decision-makers while handling the complexity of data processing internally.
3Loss of information
If conventional software solutions are used, then implementation cost is reduced, but ability to handle complex varied inputs and provide real-time insights deteriorates
Solution Approach 1:
The patent implements continuous monitoring and analysis of project data through the machine learning system. The system continuously processes project information from multiple sources, continuously updates its predictions and recommendations, and continuously provides real-time insights. This continuous operation ensures that the system always has the most current information available for decision-making, eliminating information loss.
Data Source
AI summary
A method for creating a project manifest in a computing environment includes receiving a request related to a project, determining one or more project objectives related to the request, determining a set of constraints for the project, correlating the determined set of constraints with the one or more project objectives, evaluating the correlated set of constraints and the one or more project objectives to generate an optimized model, and creating a project manifest based on the optimized model for executing the project.