An Approach Merging the IDM-Related Knowledge

Patents are one of the main innovation knowledge sources for engineers and companies. Inventive Design Method (IDM) – results from a research that extends from TRIZ and contains formal knowledge description components using ontologies, such as problems, partial solutions, and parameters. In this paper, we introduce IDM-Similar model that extends existing research work in IDM-related knowledge. A neural network named Word2vec and cosine similarity approach are used to build this model to compute the similarity among problems in wide range domains’ patents covering from the chemistry to mechanics and the computer to physics. Our model assumes that a partial solution of a patent could be used to solve the problem of another patent from a different domain if these two problems are similar enough. Experiments show that our model is a promising alternative to classical TRIZ for engineers to associate their problems in a field to solutions from patents of another field. Consequently, the step dedicated to solution concepts ideation is improved using our work

Automated Extraction of Knowledge Useful to Populate Inventive Design Ontology from Patents

International audienceThis chapter proposes an approach to extract and manage knowledge from patent documents for use by design engineers within the framework of the inventive design method (IDM). IDM is an extension of TRIZ, the theory of inventive problem solving, and is meant for complex situations. It uses generic linguistic markers to locate and extract IDM-related knowledge, such as problems, partial solutions, and parameters, to automatically populate IDM ontology