PatchNet demonstrates a promising prospect of using machine learning to classify Linux kernel patches. It identifies bug-fixing patches, which should be back ported to long-term stable versions, from all patches in the mainline Linux kernel. This tool may greatly lighten the burden and reduce the omission of picking patches for maintainers. However, there are still some obstacles for engineering applications. We present PTracer, a Linux kernel patch trace bot based on an improved PatchNet. PTracer continuously monitors new patches in the git repository of the mainline Linux kernel, filters out unconcerned ones, classifies the rest as bug-fixing or non bug-fixing patches, and report bug-fixing patches to kernel experts of commercial operating systems. As a part of PTracer, kernel experts’ feedback information is collected and used to retrain the neural network periodically for improving performance. We use the patches in February 2019 of the mainline Linux kernel to perform the test. As a result, PTracer recommended 151 patches to CGEL kernel experts out of 5,142, 102 of which were accepted. Our PTracer is the first patch trace bot successfully applied to a commercial operating system and has the advantage of improving software quality and saving labor cost.