Component Recommendation Engine for Design Discovery
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Solution Overview
Problem
Designers face challenges in discovering new parts and components with emerging functionalities due to limited awareness and inefficient search processes, often relying on ad hoc methods rather than systematic approaches.
Innovation Solution
A component recommendation engine that captures user context through natural-language queries, auto-complete prompts, and interactive interfaces, utilizing a knowledge graph to provide design recommendations based on desired functions, industries, and applications, incorporating user feedback and data from various sources to refine suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If designers rely on ad hoc search methods to discover new components, then they can find specific parts based on known specifications, but they fail to become aware of new parts with new functionality
Solution Approach 1:
The system implements feedback loops where user search queries and interactions are analyzed to understand design context and preferences. This feedback is used to continuously improve recommendation accuracy and provide designers with relevant new components that match their work patterns and requirements
Solution Approach 2:
The recommendation engine enables designers to automatically discover relevant components through an automated system that understands their design context without requiring manual searching. The system serves itself by learning from user behavior and autonomously providing personalized component recommendations
2Reliability
If designers perform detailed searches for specific parts based on specifications, then they can meet functional requirements, but the search process becomes time-consuming and burdensome
Solution Approach 1:
The system performs preliminary analysis of user queries and design context before the actual component selection process. By pre-processing and understanding the design requirements upfront, the system can quickly retrieve and rank relevant components, saving time during the actual search process
Solution Approach 2:
The patent replaces manual mechanical searching processes with an automated information retrieval system. The recommendation engine uses computational algorithms to substitute the time-consuming manual search process, maintaining functional requirement satisfaction while dramatically reducing search time
3Adaptability or versatility
If the recommendation engine provides comprehensive design recommendations with multiple attributes, then it satisfies user needs better, but the system complexity increases
Solution Approach 1:
The recommendation system is segmented into distinct functional modules including query processing, context analysis, component retrieval, and recommendation generation. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while providing comprehensive recommendations
Data Source
AI summary
Methods and system for recommending components to a user. Implementations are directed to receiving, from a user, a user query for a design recommendation, the user query comprising a plurality of terms; determining, from the plurality of terms, a user intent including at least one query-derived design function and not including a named component for the design recommendation; receiving additional contextual information for the design recommendation, wherein the additional contextual information comprises a plurality of action attributes for the at least one query-derived design function; determining, from a database, a plurality of design recommendations, wherein each design recommendation comprises a respective recommended component and a plurality of recommended component actions; selecting, from the plurality of design recommendations, a subset of design recommendations; and providing, to the user, the subset of design recommendations.