Knowledge Association Based Patent Valuation Model And Algorithm Research

With the intensification of global intellectual property competition,China has vigorously implemented the innovation-driven development strategy under the "new normal" of economic development,so the national scientific and technological innovation capabilities have been greatly improved,and the awareness of intellectual property rights has increased significantly.As an important form of intellectual property rights,patents have always attracted attention to their latent economic value.As an important bridge between the development of patent technology and patent industrialization,patent valuation is extremely important for patent transactions,patent investment,and patent transformation.However,the current automation patent valuation is still in the preliminary stage.The following challenging issues need to be resolved: how to represent the valuation object,how to construct a valuation scenario,and how to generate patent value.To solve those challenges,it is necessary to follow these steps.First,to represent the valuation object,this article focuses on the structured part and unstructured text content of the patent,and extracts some features from it to represent a valuation object.Then,because patent data and market data have complex knowledge associations,the patent value is implicit in these complex knowledge associations.Therefore,from the perspective of knowledge relevance,this study constructs a heterogeneous knowledge association network composed of patent data and market data,and uses it as a valuation scenario to provide an important calculation basis for the study of this study.In this valuation scenario,through the mutual influence between the patent and its associated market data,the value of the patent fluctuates with changes in its associated market data,resulting in a value linkage effect.Finally,on the basis of expressing good valuation objects and constructing good valuation scenarios,this article studies the generation process of patent value from two perspectives: supervised learning and unsupervised learning.1)Supervised learning: We propose a patent valuation model based on Bayesian graph convolutional neural network.In this model,we use Bayesian methods to treat the observable valuation scenario as an implementation from a random graph parameter family.Then,we reason about the posterior of the random graph parameters,and then divide them into communities to generate new valuation scenarios.Finally,the Bayesian graph convolutional neural network is used to generate patent value on the generated valuation scenario.2)Unsupervised learning: We propose a patent valuation model based on probability graphs.In this model,the prior distribution of their value is formed based on the characteristics of patents and market data.Then,put them in the valuation scenario,propose the value link hypothesis,and build the probabilistic graph model through the probabilistic generation process.Finally,variational inference algorithm is used to approximate the posterior distribution of patent value.In terms of the patent dataset,two models proposed in this thesis have been confirmed via comparing with the state-of-the-art model.In the evaluation and measurement,the models in this thesis outperform comparised models.