Product Model Training for Fabrication Configuration
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Solution Overview
Problem
The manual process of developing prospective products is time-consuming and limited by the information known by individual teams, lacking comprehensive data integration.
Innovation Solution
A product model is trained using information from previously developed products, including object and subject details, to predict performance and recommend configurations for new products, reducing errors and accelerating development by generating instructions for fabrication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual research and input processes are used to determine product configurations, then human expertise and creativity are leveraged, but the process becomes time-consuming and limited by individual knowledge boundaries
Solution Approach 1:
The system enables self-service by allowing the product model to automatically analyze training data, identify patterns, and generate product configurations without requiring continuous manual intervention. The model serves itself by processing historical product information and autonomously determining optimal configurations for new products, significantly reducing the time investment required from human researchers.
Solution Approach 2:
The patent replaces the mechanical manual research process with an automated computational system. Instead of humans manually searching through databases and analyzing product information, the system uses algorithms to process training data, identify correlations, and generate product configurations automatically, thereby eliminating time-consuming manual operations.
2Adaptability or versatility
If manual processes are used to determine product configurations, then flexibility in creative decision-making is maintained, but the scope is limited to information known by individual teams
Solution Approach 1:
The product model serves multiple functions: it analyzes various types of training data (product information, subject details, performance metrics), identifies patterns across different product categories, and generates configurations for diverse product types. This multi-functional capability allows the system to handle a universal range of product development tasks while accessing comprehensive data from multiple sources, overcoming the limitations of individual team knowledge.
Solution Approach 2:
The trained product model acts as an intermediary between the available training data and the product configuration output. It processes and synthesizes information from various sources (object information, subject information, content information, performance information) and transforms this data into actionable product configurations, thereby integrating comprehensive information that would be difficult for individual teams to access and process directly.
3Reliability
If comprehensive database information is utilized to train the product model, then accuracy and reduction of errors are improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of product configuration determination into distinct manageable components: data collection (obtaining training information), model training (processing and pattern recognition), and configuration generation (outputting product specifications). This segmentation allows each component to be developed, tested, and optimized independently, reducing overall system complexity while maintaining high reliability through comprehensive data utilization.
Data Source
AI summary
Systems and methods configured to train a product model to determine a configuration of a prospective product are disclosed. Exemplary implementations may: obtain a product model; obtain training information from electronic storage; train the product model using the training information for individual developed products by using object information, subject information, and subject content information for the individual developed products as training inputs and product performance information as training outputs for the individual developed products such that the product model is trained to predict product performance information based on object information, subject information, and subject content information; store the trained product model to the electronic storage; receive target information for a prospective product; transmit the target information to the trained product model; receive object information and/or subject information for the prospective product; generate and transmit instructions for a fabrication system to generate the prospective product according to the received information.