Intelligent collaborative patent mining using excessive topic generation

Abstract

An inevitable consequence of the technology-driven economy has led to the increased importance of intellectual property protection through patents. Recent global pro-patenting shifts have further resulted in high technology overlaps. Technology components are now spread across a huge corpus of patent documents making its interpretation a knowledge-intensive engineering activity. Intelligent collaborative patent mining facilitates the integration of inputs from patented technology components held by diverse stakeholders. Topic generative models are powerful natural language tools used to decompose data corpus topics and associated word bag distributions. This research develops and validates a superior text mining methodology, called Excessive Topic Generation (ETG), as a preprocessing framework for topic analysis and visualization. The presented ETG methodology adapts the topic generation characteristics from Latent Dirichlet Allocation (LDA) with added capability to generate word distance relationships among key terms. The novel ETG approach is used as the core process for intelligent collaborative patent mining. A case study of 741 global Industrial Immersive Technology (ITT) patents covering inventive and novel concepts of Virtual Reality (VR), Augmented Reality (AR), and Brain Machine Interface (BMI) are systematically processed and analyzed using the proposed methodology. Based on the discovered topics of the IIT patents, patent classification (IPC/CPC) predictions are analyzed to validate the superior ETG results.