Binary Partition Ideation Framework (BPI Framework)

Binary Partition Ideation Framework (BPI Framework) proposes a structural account of conceptual novelty grounded in the partition of conceptual space. Whereas many established theories of creativity describe novelty as emerging through search, transformation, or expansion within a predefined conceptual domain, BPI redefines the generative condition of novelty as a structural consequence of partition itself.

The framework begins with an operationally defined recognized set (S) within a conceptual universe (U). From this definition, its complement (S^C) follows necessarily as a logical consequence. Rather than attempting to directly represent or explore this unrecognized complement, BPI introduces exclusion conditions defined relative to S. Novel conceptual candidates are derived structurally through this complement-based mechanism.

In contrast to search-based accounts of creativity and expansion-oriented design theories such as C-K theory, BPI does not rely on iterative extension of known knowledge nor traversal of a fixed search space. Instead, it induces novelty by structurally transferring the focus of exploration through the act of partition. In this sense, novelty is not discovered within a space but generated through reconfiguration of its structural boundaries.

The framework also distinguishes between the generation of novelty and its evaluation. While creativity is commonly defined as requiring both novelty and usefulness, BPI addresses the structural conditions of novelty generation, treating evaluative judgment as a separable and subsequent stage.

Because the model is defined in terms of explicit structural operations—set partition, complement, and exclusion conditions—it is inherently compatible with computational interpretation. BPI therefore provides a theoretical bridge between creativity research and computational creativity, offering a formally interpretable model applicable to artificial generative systems.

This work positions BPI as a structural theory of concept generation within creativity research, while remaining readily transferable to computational and artificial contexts.