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To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation.

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

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