Graph Representation Learning Towards Patents Network Analysis

TL;DR

This paper presents a novel graph representation learning approach to analyze Iranian patent data, uncovering industry trends, research areas, and stakeholder connections to aid patent management and innovation.

Contribution

It introduces a new method combining natural language processing, entity resolution, and graph algorithms to analyze patent data from scratch, specifically applied to Iranian patents.

Findings

Identified new industry and research areas from Iranian patent data.

Enabled detection of similar and connected inventions.

Provided insights into legal entities and stakeholders involved.

Abstract

Patent analysis has recently been recognized as a powerful technique for large companies worldwide to lend them insight into the age of competition among various industries. This technique is considered a shortcut for developing countries since it can significantly accelerate their technology development. Therefore, as an inevitable process, patent analysis can be utilized to monitor rival companies and diverse industries. This research employed a graph representation learning approach to create, analyze, and find similarities in the patent data registered in the Iranian Official Gazette. The patent records were scrapped and wrangled through the Iranian Official Gazette portal. Afterward, the key entities were extracted from the scrapped patents dataset to create the Iranian patents graph from scratch based on novel natural language processing and entity resolution techniques. Finally, thanks to the utilization of novel graph algorithms and text mining methods, we identified new areas of industry and research from Iranian patent data, which can be used extensively to prevent duplicate patents, familiarity with similar and connected inventions, Awareness of legal entities supporting patents and knowledge of researchers and linked stakeholders in a particular research field.