Organizing large-scale patent corpora according to classification schemes is a core information management task that determines the accuracy and efficiency of prior art retrieval, technology knowledge discovery, and intellectual property decision-making. Recent approaches distill natural language rationales generated by large language models (LLMs) into compact student models, yet logical errors, label mismatches, and taxonomy misalignments inherent in these rationales are indiscriminately absorbed during training, undermining classification reliability and propagating errors throughout downstream information processes. Rather than correcting such errors post-hoc, we propose Self-Filtered Distillation (SFD), which embeds quality assurance directly into the learning process by reinterpreting LLM-generated rationales as trust indicators rather than ground-truth supervision. SFD integrates three unsupervised signals into a unified trust score that dynamically modulates each training instance's contribution: Self-Consistency, which quantifies agreement among independently generated rationales; Class Entailment Alignment, which evaluates semantic coherence between a rationale and its assigned CPC class definition; and LLM Agreement Scoring, which assesses external plausibility through an independent verifier. On the USPTO-2M benchmark comprising over two million patents, SFD achieves up to 38.7\% relative improvement in Macro-F1 across four student architectures, and the strong correlation between trust scores and expert judgments ($r = 0.685$) confirms that the framework provides not only accurate predictions but also decomposable confidence semantics that enable auditable and self-documenting classification outcomes for large-scale patent knowledge organization.