AI-assisted inventions in the field of drug discovery: readjusting the obviousness analysis

2020 ◽  
pp. 7-21
Author(s):  
Dr Olga Gurgula (Ph.D., LLM)
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Rapid Evolution ◽  
Patent Law ◽  
Legal Issues ◽  
Patent System ◽  
Computational Power ◽  
Human Capabilities

Artificial intelligence (‘AI’) is increasingly applied at all stages of drug discovery. While AI has the potential to boost innovation, it also raises many important ethical, social, political, and legal issues. Among the latter are the challenges that AI poses for the patent system. With the rapid evolution of AI technologies and the increase in their computational power, the process of inventing has undergone substantial changes. As AI significantly expands human capabilities, inventions that were previously the result of human ingenuity, perseverance or serendipity can now be achieved by routine experimentations with the use of AI. This article argues, therefore, that the patent law approaches that were developed to assess human-generated inventions are not suitable for AI-assisted inventions and requires urgent reconsideration. It will explain that the proper test for the obviousness assessment needs to take into account the advancement of AI technology and will provide suggestions on how the analysis can be modified.

Digitalized Invention, Decentralized Patent System

2021 ◽  
pp. 120-144
Author(s):  
Feroz Ali
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Digital Photography ◽  
Patent Law ◽  
Patent System ◽  
Patent Office ◽  
Working Models ◽  
System P ◽  
History Of

Throughout the history of patent law, the manner of representation of invention influenced the process of the patent office in prosecuting them. This chapter traces how changes in the representation of the invention—from material to textual to digital—transformed patent prosecution. Early inventions were represented by working models, the materialized invention that needed little or no examination by the patent office, as they were the inventions themselves. Substantive examination became necessary when the representation of the invention shifted from material to textual, the point in history where the invention became textualized and represented by the patent specification, the written document that encompassed the invention. The textualized invention, apart from effecting critical changes in patent prosecution, centralized the operations of the patent office. With the adoption of new technologies like blockchain and artificial intelligence (AI), the manner of representation of invention will undergo yet another change resulting in the further evolution of patent prosecution. Like digital photography which changed the representation of images by radically changing the backend process, the digitalized invention will change the backend process of the patent office, ie, patent prosecution. The most significant systemic consequence of the digitalization of the invention will be the decentralization of patent system.

Legal Issues of Artificial Intelligence and Patent

2018 ◽  
Vol 90 ◽  
pp. 197-231
Author(s):  
Youngsun Cho
Keyword(s):  
Artificial Intelligence ◽  
Legal Issues

Current Scenario in Structure and Ligand-Based Drug Design on Anti-colon Cancer Drugs

2019 ◽  
Vol 24 (32) ◽  
pp. 3829-3841 ◽  
Author(s):  
Lakshmanan Loganathan ◽  
Karthikeyan Muthusamy
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Anticancer Agent ◽  
Computational Power ◽  
Cancer Drugs ◽  
Recent Advances ◽  
Anti Cancer ◽  
Drug Designing ◽  
Current Scenario ◽  
Molecular Modeling Studies

Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field and the current scenario in drug designing of cancer drugs are discussed. This review provides information on how cancer drugs were formulated and identified using computational power by the drug discovery society.

International Seminar – Contributions to the Development Agenda on Intellectual Property Rights

2008 ◽  
Vol 10 (2) ◽  
Author(s):  
Ana Celia Castro ◽  
Maria Beatriz Amorim Bohrer
Keyword(s):  
Developing Countries ◽  
Intellectual Property ◽  
Property Rights ◽  
Intellectual Property Rights ◽  
Patent Law ◽  
Patent System ◽  
International Seminar ◽  
Development Agenda

TRIPS as it stands is against the interests of developing countries, and needsreform. In developing their own patent law, developing countries need to recognizethat there is now near consensus among informed observers that patentlaw and practice have, in some cases, overshot, and need to be reformed. Thatis the burden of the recent NAS/NRC report on “A Patent System for the 21stCentury.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches

2019 ◽  
Vol 59 (11) ◽  
pp. 4587-4601 ◽  
Author(s):  
Zhenxing Wu ◽  
Tailong Lei ◽  
Chao Shen ◽  
Zhe Wang ◽  
Dongsheng Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cytochrome P450 ◽  
Drug Discovery ◽  
Reliable Prediction ◽  
Human Cytochrome

scholarly journals Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

2021 ◽  
Author(s):  
Manish Kumar Tripathi ◽  
Abhigyan Nath ◽  
Tej P. Singh ◽  
A. S. Ethayathulla ◽  
Punit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery

Ten Assumptions About Artificial Intelligence That Can Mislead Patent Law Analysis

2021 ◽  
Author(s):  
Daria Kim ◽  
Maximilian Alber ◽  
Man Wai Kwok ◽  
Jelena Mitrovic ◽  
Cristian Ramirez-Atencia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patent Law

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

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