Bayesian Graph Convolutional Neural Network based Patent Valuation Model

Weidong Liu, Xin Liu, Wenbo Qiao

Abstract: With the intense competition of global intellectual property, the increasing patents promote the potentiality of patent transactions. Patent valuation is the premise of the patent transaction. Automatic patent valuation faces some challenging issues from valuation feature to valuation model. To solve the above issues, we propose a Bayesian graph convolutional neural network based patent valuation model. In the model, the valuation objects are defined, from which to some valuation features are extracted. Valuation scenario is the constructed, on which Bayesian graph convolutional neural network is used to generate patent value. We evaluate our model by comparing the state-of-the-art model on patent data sets. The results show that our model outperforms other models in the evaluation measurements.