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In telecommunication technology, identifying standard-essential patents (SEPs) plays a crucial role in the management of intellectual property. This technology is regulated by technical standards that are largely based on the content of SEPs. These patents are declared standard-essential by their owners because they contain elements of a technical standard. The declaration process leaves room for over- and under-declaration, which entails risks for organizations. This paper focuses on the question of how generative artificial intelligence can be used to assess the standard-essentiality of 5G technology patents. For this purpose, the standard-essentiality is assessed using different prompts with four Large Language Models (LLMs) in two variants. In the first variant, the LLM results are generated by a rather simple prompt and compared with an approach based on unsupervised and supervised machine learning. The result shows that large LLMs are capable of assessing the standard-essentiality. In the second variant, the best-performing LLM is selected and the prompt is expanded to include selected parts of a technical standard. While the assessment results remain largely the same, the LLM is now able to explain in which detail a patent is part of a standard. This has several implications for patent evaluation, licensing and litigation strategies.
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Stored only when both papers are in the corpus — 7 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.