AI for Patent Essentiality Review

23 Pages Posted: 10 Dec 2022

See all articles by Katie Atkinson

Katie Atkinson

University of Liverpool

Danushka Bollegala

affiliation not provided to SSRN

Date Written: November 15, 2022

Abstract

In the process of developing novel standards for Information and Communication Technologies (ICT), an important step is to determine whether a patent held by a company is, or might be, required to practice the concepts covered in a given ICT specification. The patents that claim inventions that are necessary to practice a particular ICT standard are called Standard Essential Patents (SEPs) [Baron and Pohlman, 2021]. Existing approaches for automatically detecting SEPs for a given specification rely on textual similarity measures such as Latent Semantic Analysis (LSA) [Landauer and Dumais, 1997, Deerwester et al., 1990]. In this report, we first give an overview of the task of manually detecting SEPs as conducted by patent lawyers and subject matter experts, in section 2. Next, we will discuss the associated challenges from the view point of the state-of-the-art (SoTA) in Artificial Intelligence (AI), in section 3. We provide a general overview of how AI has been applied to the domain of Law in section 4, including specifically the applications of AI in Patent Law. We discuss existing tools that purport to facilitate patents that are essential for a given ICT specification in section 5. We summarise in section 6 SoTA developments in the Machine Learning (ML) and Natural Language Processing (NLP) communities that can potentially address the challenges discussed in section 3. Finally, we conclude this report in section 7 by providing a set of recommendations from a technological perspective and we list requirements that must be satisfied by future solutions to the SEP detection problem such that more accurate and explainable tools can be developed.

Keywords: AI and Law, Standard Essential Patents

Suggested Citation

Atkinson, Katie and Bollegala, Danushka, AI for Patent Essentiality Review (November 15, 2022). Available at SSRN: https://ssrn.com/abstract=4277799 or http://dx.doi.org/10.2139/ssrn.4277799

Katie Atkinson (Contact Author)

University of Liverpool ( email )

Danushka Bollegala

affiliation not provided to SSRN

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