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Patent litigation is an expensive legal process faced by many companies. To reduce the cost of patent litigation, one effective approach is proactive management based on predictive analysis. However, automatic prediction of patent litigation is still an open problem due to the complexity of lawsuits. In this paper, we propose a data-driven framework, Convolutional Tensor Factorization (CTF), to identify the patents that may cause litigations between two companies. Specifically, CTF is a hybrid modeling approach, where the content features from the patents are represented by the Network embedding-combined Convolutional Neural Network (NCNN) and the lawsuit records of companies are summarized in a tensor, respectively. Then, CTF integrates NCNN and tensor factorization to systematically exploit both content information and collaborative information from large amount of data. Finally, the risky patents will be returned by a learning to rank strategy. Extensive experimental results on real-world data demonstrate the effectiveness of our framework.

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Micro/nano-structured coatings with antibacterial function were prepared by microarc oxidation (MAO) treatment on Ti6Al4V alloy in a silicate/phosphate electrolyte with a NaF additive. The microstructure, phase composition, and corrosion resistance of the coatings modified by adding NaF (0.15–0.5 M) were examined using scanning/transmission electron microscopy, energy dispersive spectroscopy, atomic force microscopy, X-ray diffraction, and potentiodynamic polarization. The results showed that the incorporation of F ion reduced the threshold voltage for electrons avalanche on the surface film of the Ti alloy, and increased the intensity and lifetime of discharge. MAO coatings with 100–500 nm nano-pores and 1–20 [Formula: see text]m micro-pores were formed by the modification of the NaF additive. The F ions promoted microarc discharge as well as phase transformation from the metastable anatase to the stable rutile phase. The F ions also promoted the generation of penetration cracks and bubbles in the coating. The surface roughness, phase content, and thickness of the coating were enhanced by the NaF additive. However, the corrosion resistance of the coating first increased and then decreased with the increasing F ion concentration, reaching a maximum when the NaF content was 0.25 M.

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