Project Details
Funding Scheme : General Research Fund
Project Number : 17615924
Project Title(English) : Artificial Intelligence in the Administration of the Patent System 
Project Title(Chinese) :  
Principal Investigator(English) : Dr Whalen, Ryan Seamus McGrath 
Principal Investigator(Chinese) :  
Department : Department of Law
Institution : The University of Hong Kong
Co - Investigator(s) :
Panel : Humanities, Social Sciences
Subject Area : Social and Behavioural Sciences
Exercise Year : 2024 / 25
Fund Approved : 366,626
Project Status : On-going
Completion Date :
Abstract as per original application
The patent system is economically integral (Dam, 1994; Khan, 2005; Kitch, 1977). Yet, despite its importance, it faces myriad challenges and is described by some innovation scholars as “broken” (Jaffe & Lerner, 2011) or “failing” (M. D. Frakes & Wasserman, 2019). Many of these challenges arise because of the sheer amount of information that must be analyzed to examine patent applications, and the difficulty that applicants and patent examiners have in dealing with this vast and expanding information universe as they determine whether a claimed invention is patentable. Artificial intelligence (AI) excels at dealing with large sets of data and offers the prospect for efficiency gains in the patent system if implemented appropriately. This project addresses two linked sets of questions: how might this implementation take place and what are the potential benefits and risks that automated administration of the patent system might bring? In this project’s first stage I will engage with two topics: why policymakers should consider adopting increased use of AI to administer the patent system, and how best to implement it to further the system’s goals. This stage of the project will detail and summarizes the challenges facing the patent system and explore how machine learning and AI might help address those challenges. It will then take a theoretically, and empirically driven perspective on how to go about engineering technologies to improve the patent system’s efficiency. In the second stage I will empirically demonstrate the capacity of AI to make labour intensive patent examination tasks more efficient. This will entail producing demonstration models allowing users to explore how different methods of automating patentability classifiers affect the classification results. Comparing different approaches, such as a naïve metadata correlational approach, with more sophisticated text-based approaches, will help reveal the tradeoffs inherent in engineering legal decision-making aides. In the final stage, I will build on the first two by critically examining the effects of automated decision-making aides on the innovation system. The adoption of technologically-aided decision-making technologies is not a new phenomenon, but recent improvements in the abilities of machine learning techniques have enabled technologies that could fundamentally change many elements of legal and administrative decision-making. This final stage will explore these possible futures and discuss what we can learn from the patent system’s adoption of artificial intelligence technologies to aide in patent examination.
Research Outcome
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