Regularized artificial neural network based patent value interval prediction model

With the increasing competition in intellectual property rights, patent value prediction has become a hot research issue in the field of intellectual property rights. Most of the current research on patent value either predicts some structural indicators (e.g., citations) or the intrinsic value of patents, but there is relatively little work on predicting the actual value of patents. Patent value prediction faces the following challenges, including: (1)data: the amount of actual transaction data of patents is small and difficult to collect, so the amount of training data and test data can be used is small; (2)features: how to extract the valuation features of the patent, especially technical features from unstructured patent texts; (3)model and validation: how to build a model for predicting patent prices and how to verify the accuracy of our experimental results. To solve these problems, we propose a regularized artificial neural network based patent value interval prediction model: (1) in terms of data, we extract the value data of patents from the official websites of several universities in China, the oceantomo patent trading platform in the United States, and the Patsnap platform, which come with information such as the actual transaction price or the bidding and asking price. (2) in terms of features, we extract the unstructured textual features of patent portfolios, technical features, and structured features of fifteen patents as the valuation features of patents; (3) in terms of model and validation, we use regularized artificial neural networks to give prediction intervals and prediction uncertainty, and validate the model with a measure of numerical intervals. We compare the model with the baseline model and the results show that our model achieves good results.