Data-Driven Models for Product Space Exploration
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Solution Overview
Problem
The creative process for product development in industries like food and flavor production is constrained by the large and exponentially complex product space, leading to repetitive product creation and inefficiencies, as experts often rely on past experiences without analytical tools to explore and characterize this space effectively.
Innovation Solution
A data-driven approach combining machine learning, expert feedback, and computational tools to characterize and quantify the product space, guide the creation of new products, and provide managerial insights, using algorithms to determine similarity and dissimilarity between products and ingredients, and suggest new products or improvements based on predefined goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If experts rely on past experiences without analytical tools to explore product space, then product development can proceed with simple methods, but the product creation process becomes constrained and repetitive
Solution Approach 1:
The patent introduces computational tools and machine learning algorithms as intermediaries between expert knowledge and product development. These tools analyze product space, calculate similarity metrics, and provide data-driven recommendations, enabling experts to efficiently explore and characterize product space without being constrained by reliance on past experiences alone
Solution Approach 2:
The patent replaces the mechanical reliance on human expert memory and experience with automated computational systems. Machine learning models process product data, calculate similarity metrics, and generate insights that would be impossible to obtain through human analysis alone, thereby eliminating the constraint of repetitive product creation
2Adaptability or versatility
If the product space is large and exponentially complex, then product diversity is possible, but exploring and characterizing the space becomes difficult
Solution Approach 1:
The patent replaces manual exploration and characterization methods with machine learning-based computational systems. These systems automatically process large volumes of product data, calculate similarity metrics across the product space, and generate structured insights, making it feasible to explore and characterize exponentially complex product spaces that would be intractable through traditional methods
Solution Approach 2:
The patent transforms the complex product space into a structured representation by defining similarity metrics and distance measures. By changing the parameters of analysis from raw product attributes to calculated similarity scores and spatial relationships, the system makes the product space explorable and characterizable through computational means
Data Source
AI summary
Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.