Generative artificial intelligence systems not only produce expressive outputs, but also provide rich latent spaces, i.e., mathematical objects that express the semantic relation among the training data. These unique representational strata represent creative associations and redefine the traditional limits of copyright law. The article introduces the concept of Latent Copyrights, which proposes the provision of copyright protection to intermediate representations of the products generated by AI. It examines the use of computational creativity for the purpose of copyright protection. Engaging in a comparative analysis of the copyright regimes of the U.S., U.K., and E.U., this study suggests a relative system to ensure the protection of innovation, interoperability, and responsibility. The aim is to streamline the Latent Copyright theory of intellectual property with the technicalities of machine learning by offering a paradigm that conceptualizes representational layers of an AI-generated product as a medium of Copyright.