  Combinational creativity, a form of creativity involving the blending of
familiar ideas, is pivotal in design innovation. While most research focuses on
how combinational creativity in design is achieved through blending elements,
this study focuses on the computational interpretation, specifically
identifying the 'base' and 'additive' components that constitute a creative
design. To achieve this goal, the authors propose a heuristic algorithm
integrating computer vision and natural language processing technologies, and
implement multiple approaches based on both discriminative and generative
artificial intelligence architectures. A comprehensive evaluation was conducted
on a dataset created for studying combinational creativity. Among the
implementations of the proposed algorithm, the most effective approach
demonstrated a high accuracy in interpretation, achieving 87.5% for identifying
'base' and 80% for 'additive'. We conduct a modular analysis and an ablation
experiment to assess the performance of each part in our implementations.
Additionally, the study includes an analysis of error cases and bottleneck
issues, providing critical insights into the limitations and challenges
inherent in the computational interpretation of creative designs.
