To file a patent or examine a submitted patent, one must perform a prior-art search that includes both patent and non-patent literature. Unlike patent literature, non-patent literature is not standardized and lacks a unified search system, thus necessitating separate searches for patents and non-patents. This renders the process particularly challenging for the latter. Hence, classification methods used in patent literature are applied to non-patent literature in this study, thus enabling a search system that operates in the same manner as patent-literature searches. The proposal includes the application of machine-learning techniques to recommend or automatically assign patent-classification codes to non-patent literature. For example, a process is reviewed in which international patent classificationcodes are automatically assigned to scholarly papers using machine-learning algorithms. Based on analyzing methods that leverage text-similarity and text-classification algorithms, the automatic classification of non-patent literature through patent-literature text mining is shown to be effective and thus warrants further research. Building a database of non-patent literature coded with patent classifications can result in a more efficient prior-art search process by allowing searches under a unified classification system for both patent and non-patent literatures.