Using AI to Valuate and Determine Essentiality for SEPs

AI-based semantic claim section comparisons and cross correlation of inventor participation and the submission of accepted technical contribution at standards meetings are strong indicators of patents being relevant to a given standard and can be integrated as features in AI-based SEP prediction models that score patents as to their likelihood of being standard essential. Making use of verified SEP training data from expert claim charts allows extrapolating information about essentiality to a much larger set of patents. This allows valuating and determining large patent portfolios that are economically not feasible to be manually mapped by experts. Furthermore, AI-based SEP prediction models allow estimating the likelihood of SEP essentiality for patents that have not even been declared due to blanked declaration statements.

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