AI Design Decision Framework for Big Data Retrieval
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Solution Overview
Problem
Current technologies face challenges in optimally utilizing data from past design projects to aid future designs, particularly in engineering and mission planning, where complex data analysis and rapid information retrieval are necessary for efficient decision-making.
Innovation Solution
A framework utilizing big data analytics and artificial intelligence to harvest repositories of known good designs, combined with a querying engine based on latent semantic analysis, provides relevant design information for improving design decision fidelity and efficiency across engineering, mission, and retail planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used for design projects, then data processing is simpler, but design decision fidelity and efficiency are insufficient
Solution Approach 1:
The patent replaces traditional mechanical data analysis methods with artificial intelligence and machine learning systems. The AI framework automatically processes design data, extracts patterns, and generates insights without manual intervention, thereby improving design decision fidelity while managing complexity through automation rather than human analysis capabilities.
Solution Approach 2:
The patent introduces an AI-based intermediary system that sits between raw design data and decision-makers. This intermediary automatically processes, filters, and interprets large volumes of design data, transforming it into actionable insights that improve decision fidelity without requiring end-users to directly handle the complexity of raw data analysis.
2Reliability
If comprehensive data from past design projects is harvested and analyzed, then design insights improve, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and structuring design data as it is generated or archived. The AI system continuously learns from past design projects, maintaining updated models and patterns ready for rapid retrieval and application to new design challenges, thereby reducing processing time when insights are needed without compromising comprehensive data analysis.
Solution Approach 2:
The patent implements continuous data processing and learning through the AI framework. Rather than batch-processing data periodically, the system continuously ingests, analyzes, and learns from design data streams, maintaining up-to-date insights and patterns that can be immediately applied, thus reducing the effective processing time for design decisions while maintaining comprehensive analysis.
3Productivity
If AI and big data analytics are implemented for design projects, then design decision fidelity improves, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a universal AI framework that can handle multiple design domains and data types through a single integrated system. The framework is designed to be domain-agnostic, capable of processing various kinds of design data (engineering, architectural, product design) using the same core architecture, thereby improving design efficiency without proportionally increasing implementation complexity through standardization and reusability.
Data Source
AI summary
This invention presents a framework for applying artificial intelligence to aid with product design, mission or retail planning. The invention outlines a novel approach for applying predictive analytics to the training of a system model for product design, assimilates the definition of meta-data for design containers to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with relational dependence. Design information can be harvested, for the purpose of improving decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented. This model, which is based on latent semantic analysis, predicts the degree of relevance between accessible design information and a query, and presents the most relevant previous design information to the user.