AI Recommendation Engine for Engineering Project Completion
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Solution Overview
Problem
Configuring complex engineering projects, such as automated systems, is a time-consuming and knowledge-intensive task that requires domain-specific expertise, making it challenging for less experienced users to complete correctly.
Innovation Solution
A recommendation engine utilizing artificial intelligence modules to generate latent representations and sequences of complementary items for completing engineering projects, leveraging trained neural networks to suggest next items or complete sequences based on historical data and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually configures components of a complex engineering project, then the user can select and customize each component, but the process requires significant time, effort, and domain-specific knowledge
Solution Approach 1:
The patent replaces the manual mechanical process of component selection and configuration with an automated AI-based recommendation system. The system uses machine learning models to analyze project requirements and automatically suggest optimal component combinations, substituting human manual configuration operations with automated computational processes.
Solution Approach 2:
The recommendation system enables users to configure complex engineering projects independently without requiring extensive domain expertise. The AI system provides self-service by automatically analyzing requirements and generating configuration recommendations, allowing users to complete configuration tasks on their own without needing expert knowledge.
2Adaptability or versatility
If a user manually configures components of a complex engineering project, then the user can customize the system to specific needs, but the process requires significant effort and domain-specific knowledge
Solution Approach 1:
The patent introduces an AI recommendation system as an intermediary between user requirements and component configuration. This intermediary automatically analyzes project needs and translates them into appropriate component selections and configurations, bridging the gap between user intent and technical implementation without requiring users to navigate complex configuration processes directly.
Solution Approach 2:
The configuration process is segmented into distinct stages: requirement analysis, component recommendation, and configuration assembly. The AI system handles the complex analysis and recommendation generation separately from the final configuration implementation, breaking down the overall complexity into manageable segments that users can interact with more easily.
3Reliability
If traditional configuration methods are used, then users have full control over component selection, but less experienced users struggle to complete configuration correctly
Solution Approach 1:
The recommendation system incorporates feedback mechanisms where the AI model continuously learns from user interactions, configuration outcomes, and project success metrics. This feedback loop enables the system to improve its recommendations over time, increasing configuration accuracy while maintaining ease of use for users with varying levels of expertise.
Solution Approach 2:
The patent replaces manual expert judgment and experience-based configuration decisions with automated AI-driven recommendation algorithms. This substitution ensures consistent, accurate configurations based on learned patterns from historical data, removing the variability and errors associated with human expertise limitations.
Data Source
AI summary
Provided is a recommendation engine to provide automatically recommendations for the completion of an engineering project, the recommendation engine including: a first artificial intelligence, AI, module adapted to provide latent representations of a sequence of selected items; and a second artificial intelligence, AI, module adapted to process the latent representations of the sequence of selected items provided by the first artificial intelligence, AI, module to generate at least one sequence of complementary items required to complement the sequence of selected items to provide a complete sequence of items output via an interface as a recommendation to complete the engineering project.