In this paper, we present a class-conditioned Denoising Diffusion Probabilistic Model (DDPM) based approach to augment point cloud data within the latent feature space. Our method focuses on generating synthetic point cloud latent embeddings, which encode both spatial and semantic information of the point cloud. By harnessing the capabilities of DDPM within a class-conditioned framework, our goal is to provide a cost-effective and practical solution for the augmentation of point cloud samples. We conduct experiments on the publicly available point cloud dataset, and our findings suggest that the proposed approach (a) effectively generates high-quality synthetic embeddings directly from the Gaussian noise and (b) improves the classification performance of the point cloud classes within limited data settings.