Automating Multilingual Patent Intelligence Monitoring with a Low-Code Hybrid Workflow: An Engineering Case Study

Abstract

Patent intelligence is hard to automate: data is heterogeneous and multilingual, and monitoring runs on daily cycles. This paper reports an engineering case study of a production low-code workflow on the n8n platform that integrates twelve RSS feeds from five patent offices (EPO, WIPO, USPTO, TIPO, and MOIP). Over a four-month deployment (October 2025 to January 2026; an approximately 120-day window), the system processed 340 items, normalized six timestamp formats, handled English, Chinese, and Korean, and generated three stakeholder-specific output formats with no manual intervention in formatting or delivery. From this deployment, we identify five design lessons: P1 (Hybrid Intelligence Architecture), P2 (Format Normalization at Boundaries), P3 (Separation of Content and Presentation), P4 (Graceful Degradation), and P5 (Configuration Externalization), each supported by differentiated within-case evidence. Output quality was assessed exploratorily with two LLM-based evaluators, whose inter-rater agreement was low for semantic dimensions; a small expert pilot (four English-language items, four raters) provided only a preliminary reference, on which the system output did not exhibit any obvious serious errors. Applying the lessons in other domains is future work, not a contribution. The findings are documented engineering experience from a single production case.