A patent quality classification model based on an artificial immune system

Patents are business and financial assets which can enhance a company’s competitive position. Thus, patent analysis is important for defining business strategies and supporting decision-making in organizations. However, patent analysis can involve vast data sets and are difficult to analyze. The purpose of this study is to apply artificial immune system hybrid collaborative filtering to build a patent quality classification model. We apply the model to predicting the quality of radio frequency identification patents. Using a simple definition of quality, we define each patent’s data as an antigen and then compute the affinities of the target patent to all immune networks. If the affinity is larger than a given threshold, the antibody is cloned to the related immune network. After the immune networks are constructed, they exhibit high affinity to the target patent. Finally, a series of experiments show that the proposed model can accurately predict the quality of new patents. The resulting automatic patent quality classification model provides manufacturers with improved insights into their company’s intellectual property strategy, product direction and long-term vision.

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