Data-driven Product Functional Configuration: Patent Data and Hypergraph

Abstract

The product functional configuration (PFC) is typically used by firms to satisfy the individual requirements of customers and is realized based on market analysis. This study aims to help firms analyze functions and realize functional configurations using patent data. This study first proposes a patent-data-driven PFC method based on a hypergraph network. It then constructs a weighted network model to optimize the combination of product function quantity and object from the perspective of big data, as follows: (1) The functional knowledge contained in the patent is extracted. (2) The functional hypergraph is constructed based on the co-occurrence relationship between patents and applicants. (3) The function and patent weight are calculated from the patent applicant’s perspective and patent value. (4) A weight calculation model of the PFC is developed. (5) The weighted frequent subgraph algorithm is used to obtain the optimal function combination list. This method is applied to an innovative design process of a bathroom shower. The results indicate that this method can help firms detach optimal function candidates and develop a multifunctional product.