This study introduces a novel approach to objectively measure innovativeness and originality in product design across diverse categories, designer shoes, dresses, and residential architecture. We first develop and train custom Generative Adversarial Networks (GANs) models on extensive imagery datasets for these product types, with a primary focus on women’s designer shoes. By identifying the latent space and latent dimensions for each model, we establish a framework for quantifying design innovativeness and originality across these varied product domains. By establishing objective measures for design originality and innovativeness across diverse product categories, this study contributes to the fields of product design and innovation management. It offers a data-driven approach to understand and leverage complex relationships between design originality, consumer acceptance, and market success in various industries. Furthermore, our approach provides a scalable method to evaluate and enhance creative processes in design, while supporting profitable innovation.