Data-driven Product Functional Configuration: Patent Data and Hypergraph

Abstract

Abstract Product functional configuration (PFC) is a common way for the firm to satisfy individual requirements of the customer and be carried out base on market analysis. This study aims to help firms analyze functions and carry out function configuration by the patent data. This paper proposes a patent data-driven product function configuration method based on a hypergraph network. It constructs a weighted network model to optimize the combination of product function quantity and object from the perspective of big data: (1) The functional knowledge contained in the patent is extracted. (2) The functional hypergraph (FH) is constructed according to the co-occurrence relationship of patent and applicant. (3) The function and patent weight are calculated from the perspective of the patent applicant and patent value. (4) The weight calculation model of PFC is built. (5) The weighted frequent subgraph algorithm is used to obtain the optimal function combination list. This method is applied in the innovative design process of a bathroom shower. The result indicates that this method designed in the study has a positive effect on helping firms detach optimal function candidates and develop a multi-function product.

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