LAUSR

With the intense competition of global intellectual property, the number of authorized patents is increasing. However, the patent conversion rate is low and the patent valuation is hard. The realization of patent valuation faces some basic challenges including: (1) how to develop a patent valuation model in consideration of technical factors; (2) how to train/test the patent valuation model with the insufficient standard value data. To solve the above issues, we assume that the realization of patent valuation begins with selecting the realistic value-paths: defense, implementation or transfer. We explore a Bayesian neural network-based model to predict the paths toward the realization of patent valuation. In the model, a function-effect-based patent representation is proposed, from which some technical features are extracted. Given the patent features, we use Bayesian neural network to predict the value-paths toward the realization of patent valuation. The model is evaluated by precision, recall, F-measure. The results show our method can improve evaluation measurements significantly after the addition of technical features.