Although Case-Based Reasoning comes up in order to solve knowledge acquisition bottleneck, a case structure acquisition bottleneck emerges there in CBR instead of it. Because we cannot decide an appropriate case structure in advance, a framework for CBR should be able to improve a case structure dynamically, collecting and analyzing cases. Here is discussed a new framework for knowledge acquisition using CBR and model inference. Model Inference tries to obtain new descriptors (predicates) with interaction of a domain expert, regarding the predicates as the slots that compose a case structure, focusing on the function of predicate invention. The framework has two features: (1) CBR obtains a more suitable group of slots (a case structure) incrementally through cooperation with model inference, and (2) model inference with predicate invention capability discovers the rules which deal with a given task better. The system has been applied to the legal analogy problem to acquire new legal interpretation rules from given precedents. The system has invented two important legal predicates and generated two legal interpretation rules including some legal doctrine related to the problem. And the case structure has been improved using the two invented predicates. The experimental results show us that the framework is promising to acquire knowledge in the field of legal interpretation.