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Patent Mining by Extracting Functional Analysis Information Modelled As Graph Structure: A Patent Knowledge-base Collaborative Building Approach
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 among others. A new graph data modelling method is proposed for building a semantic database of patents from functional representations 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 to lower ontologies to more specific engineering 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/Designs comparisons is computed by search queries using counting of overlaps of different levels and weights.
This work has produced the PatMine SolidWorks Add-in ©, which compares annotated CAD designs with patents and highlights overlapping design concepts. The annotation extracts a functional analysis and its structure is represented as geometric features 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 will be implemented as plug-ins in the near future.
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