Sequential pattern mining remains a challenging task due to the large number of redundant candidate patterns and the exponential search space. In addition, further analysis is still required to map extracted patterns to different outcomes. In this paper, we introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning to find sequential patterns that occur frequently and exhibit desired properties. This allows the creation of novel pattern embeddings that are useful for knowledge extraction and predictive modeling. Based on dichotomic pattern mining, we present two real-world applications for customer intent prediction and intrusion detection. Overall, our approach plays an integrator role between semi-structured sequential data and machine learning models, improves the performance of the downstream task, and retains interpretability.