Bayesian Neural Network Based Path Prediction Model Toward the Realization of Patent Valuation

Weidong Liu, Wenbo Qiao, Xin Liu

Abstract: With the growing importance of intellectual property, the amount of patent increases every year. The patents realize their values by the patent conversion. However, many patents do not realize their values since the paths to realize the patent value have not been found. To predict the paths, we explore a Bayesian neural network based model. In the model, the patents are represented by the function-effects, from which some technical features are extracted. We use Bayesian neural network to predict the paths toward the realization of patent valuation. The model is evaluated by the evaluation measurements. The results show our method performs well in the evaluation measurements. Such model can be applied to further patent recommendation and automated trading.