  Patents provide a rich source of information about design innovations. Patent
mining techniques employ various technologies, such as text mining, machine
learning, natural language processing, and ontology-building techniques. An
automated graph data modelling method is proposed for extracting functional
representations for building a semantic database of patents of mechanical
designs. The method has several benefits: The schema-free characteristic of the
proposed graph modelling enables the ontology it is based on to evolve and
generalise to upper ontologies across technology domains and to specify lower
ontologies to more specific domains. Graph modelling benefits from enhanced
performance of deep queries across many levels of relationships and
interactions and provides efficient storage. Graph modelling also enables
visualisation libraries to use the graph data structure immediately, avoiding
the need for graph extraction programs from relational databases. Patent/Design
comparisons are computed by search queries using counting of overlaps of
different levels and weights. This work has produced the PatMine SolidWorks
Add-in \c{opyright}, which compares annotated CAD designs with patents and
highlights overlapping design concepts. The patent annotation extracts its
functional analysis, representing its structure as geometric feature
interactions. Additional features such as full-text search and semantic search
of the PatMine patents database are available, and graph analytic methods and
machine learning algorithms are enabled and can be implemented as plug-ins in
future work. Keywords: Patent Mining; Semantic Analysis; Functional Analysis
Diagrams; Graph Data Modelling; Visualisation; Similarity Scoring; Big Data
Analytics; Machine Learning; Artificial Intelligence; Natural Language
Processing
