This paper describes the Ehime-U system submitted to the Japanese–English Patent Claims Translation/Evaluation Task at WAT 2026. Our system predicts translation errors by providing an LLM with word-level correspondences between source and target claims. We compare six settings that differ in how these correspondences are obtained and whether they are used: four Unbalanced Optimal Transport (UOT)-based alignment methods (Forward, Reverse, AND, and OR), one alignment generated by LLM, and a baseline without alignment. We discuss how the choice of word correspondence affects error prediction. Against the gold reference annotations, under the hyperparameter settings used in this study, providing word correspondences improves error-detection F1 by up to 3.7 points over the no-alignment baseline (0.512 vs. 0.475). The improvement is category-specific: recall increases for alignment-dependent error categories but not for fluency errors. Comparing the two approaches to obtaining correspondences, we find that LLM-based alignment covers substantially more source tokens than the UOT alignments and achieves a higher F1 score than the best UOT setting under the hyperparameter settings used in this study (0.512 vs. 0.486).