Experimenting with a prompt-based AI examiner for inventive step determination: A rule-based framework toward harmonized patent examination

Experimenting with a prompt-based AI examiner for inventive step determination: A rule-based framework toward harmonized patent examination

Journal article

1 volume.

Inventive step (non-obviousness) is widely regarded as the most resource-intensive and unpredictable requirement in patent examination. While patent offices provide published detailed guidelines, inventive-step determinations remain vulnerable to interpretive discretion, resulting in significant inconsistency across examiners and jurisdictions. Such instability increases prosecution costs for applicants and reduces predictability in innovation-based business strategy. This paper proposes a structured reasoning framework, referred to as the pair formulas technique, which operationalizes inventive-step examination through two parameters: (i) the technical difference between a claimed invention and the closest prior art, and (ii) the benefit (effect) arising from that difference. This framework is not intended to fully reproduce actual inventive-step practice in all doctrinal and practical respects. Rather, it isolates a narrower analytical core that seeks to make the conventional “motivated to modify” inquiry more transparent, evidence-centered, and repeatable. To explore feasibility, we conducted prompt-based experiments using an off-the-shelf large language model (LLM)-based chatbot configured with strict procedural rules that emulate examiner reasoning. Because the chatbot was not fine-tuned for patent examination, perfect doctrinal compliance was not assumed at the outset. Instead, the experiments examined whether prompt-based structure, together with human verification and limited dialogue-based correction, could organize inventive-step reasoning into a more transparent and auditable process. The results suggest that prompt-based AI systems may serve as practical tools for examiners and applicants by standardizing reasoning steps, and reducing unsupported variability. At the same time, the experiments show that human verification remains necessary, particularly where element mappings or unsupported interpretive drift may arise.

Crossref

World Patent Information ; 85, June, 2026

World Patent Information

Oxford [England] : Elsevier Ltd., 2026.

English

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

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