Patent lifetime prediction using LightGBM with a customized loss.

Patent lifespan is commonly used as a quantitative measure in patent assessments. Patent holders maintain exclusive rights by paying significant maintenance fees, suggesting a strong correlation between a patent's lifespan and its business potential or economic value. Therefore, accurately forecasting the duration of a patent is of great significance. This study introduces a highly effective method that combines LightGBM, a sophisticated machine learning algorithm, with a customized loss function derived from Focal Loss. The purpose of this approach is to accurately predict the probability of a patent remaining valid until its maximum expiration date. This research differs from previous studies that have examined the various stages and phases of patents. Instead, it assesses the commercial viability of individual patents by considering their lifespan. The evaluation process utilizes a dataset consisting of 200,000 patents. The experimental results show a significant improvement in the performance of the model by combining Focal Loss with LightGBM. By incorporating Focal Loss into LightGBM, its ability to give priority to difficult instances during training is enhanced, resulting in an overall improvement in performance. This targeted approach enhances the model's ability to distinguish between different samples and its ability to recover from challenges by giving priority to difficult samples. As a result, it improves the model's accuracy in making predictions and its ability to apply those predictions to new data.

An entropy trap model of thermodynamic anomalies for dual-amorphous water undergoing liquid-liquid phase transition.

Water displays numerous anomalously thermodynamic behaviors. However, the working principles behind these anomalies are not well understood, and the liquid-liquid phase transition (LLPT) is often regarded as the potential reason. In this study, we developed an entropy trap model to characterize the thermodynamic LLPT in dual-amorphous water, i.e. having both low-density and high-density liquid water. From the Adam-Gibbs model and free-volume theory, thermodynamic behaviors of water have been described using the proposed model, in which the constitutive relationships among density, heat capacity, thermal expansivity and glass transition temperature have been formulated. Moreover, the glass transition and its connection to thermodynamic behaviors were also investigated for dual-amorphous water. Finally, experimental data reported in the literature were used to verify effectiveness of the proposed model. This study is expected to provide a physical insight into the anomalous thermodynamics of dual-amorphous water undergoing the LLPT.