Thursday, 23 March 2023

Where AI Works Well and Where it Doesn’t in Predicting Standard Essentiality for Patents

Artificial Intelligence (AI) is providing enormous productivity and increased value in many applications. Introduction of the chatbot ChatGPT has taken the interpretation of text to a much higher level. ChatGPT can understand complex instructions and provide sophisticated responses, such as essays good enough to pass university exams. The “digital twin” AI predictions of an aircraft in flight based on physics equations and mathematical models can be continuously recalibrated with accurate measurements of position, altitude, velocity, acceleration, temperature and airframe strain. 

But AI is no panacea and is not yet sufficiently well developed to be precise or dependable everywhere. For example, much better AI training data is required to reliably estimate patent essentiality to standards such as 4G and 5G, where AI is being advocated by various experts and has already been adopted by one patent pool. AI training data needs to include many accurate determinations, including of patents found essential and patents found not essential. There is no such data set.

There is also a lot of room for improvement in AI inferencing. Essentiality determination is subjective. Even competent human experts doing a thorough job often disagree about their determinations on the same patents. Technical and legal interpretations of language may differ, as does the meaning of words in different contexts, or over the years as definitions and use of language changes.

My full article on this topic was published as a guest contribution to IP Watchdog.

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