Assessing the novelty of patent claims is a critical yet challenging task traditionally performed by patent examiners. While advancements in NLP have enabled progress in various patent-related tasks, novelty assessment remains unexplored. This paper introduces a novel challenge by evaluating the ability of large language models (LLMs) to assess patent novelty by comparing claims with cited prior art documents, following the process similar to that of patent examiners done. We present the first dataset specifically designed for novelty evaluation, derived from real patent examination cases, and analyze the capabilities of LLMs to address this task. Our study reveals that while classification models struggle to effectively assess novelty, generative models make predictions with a reasonable level of accuracy, and their explanations are accurate enough to understand the relationship between the target patent and prior art. These findings demonstrate the potential of LLMs to assist in patent evaluation, reducing the workload for both examiners and applicants. Our contributions highlight the limitations of current models and provide a foundation for improving AI-driven patent analysis through advanced models and refined datasets.

This chapter explores the role of patent protection in algorithmic surveillance and whether ordre public exceptions from patentability should apply to such patents, due to their potential to enable human rights violations. It concludes that in most cases, it is undesirable to exclude algorithmic surveillance patents from patentability, as the patent system is ill-equipped to evaluate the impacts of the exploitation of such technologies. Furthermore, the disclosure of such patents has positive externalities from the societal perspective by opening the black box of surveillance for public scrutiny.

What is artificial intelligence (AI)? The term artificial intelligence (AI) describes computer-implemented approaches to emulate human decision-making structures to enable computers and machines to process and solve problems largely independently. An essential tool for being able to arrive at independent solutions is the ability of an AI system to learn. This ability is referred to as machine learning. In this process, the AI system learns because of examples to be able to generalize given patterns after the learning phase is complete.

Patent prosecutors should consider drafting claims to avoid the situation where the AI is the only entity providing an inventive contribution. Artificial intelligence (AI) is making an impact in our everyday life. AI helps us cut grass and vacuum living rooms. It helps us identify images for applications from waste sorting to medical diagnosis. AI also is making an impact in research and development.

Artificial intelligence (AI) is set to transform many aspects of our lives, including our home and health. AI is already widely used in internet searches, and home devices with speech recognition, but in the near future we will see AI become even more widespread. This will have significant repercussions as AI performs many tasks that until now could only be undertaken by humans. AI will remove human intervention from much of the picture. This will particularly affect intellectual property law.

Can patents be issued for technology or algorithms derived by artificial intelligence? That's a problem that lawyers and researchers are now grappling with. Law firm Baker McKenzie predicts that "patentability of AI-created inventions, liability for infringement by AI, and patent subject-matter eligibility of AI technologies are the top three areas of patent law that will be disrupted by AI." Kay Firth-Butterfield, Head of AI and ML at the World Economic Forum Center, said that "we are about to witness a collision between artificial intelligence and various aspects of patent law. This technology is going to change the game for many sectors, and will impact numerous regulations and legal fields." Currently patents are awarded to individuals.

On August 22, 2019, the United States Patent and Trademark Office (USPTO) published a request for comments on patenting artificial intelligence inventions. Written comments must be received on or before October 11, 2019. The AI inventorship issue came to a head earlier this year when the inventor of an algorithm named DABUS (device for the autonomous bootstrapping of unified sentience) filed beverage container and flashing light patent applications in DABUS' name in the United Kingdom, Europe, and the United States. Additionally, many today see patent eligibility as a significant hurdle to obtaining patent protection in AI technologies. China and the United States lead in patent filings in all AI techniques and functional applications, as well as AI application fields.

The patentability of artificial intelligence (AI) has been increasingly scrutinized in light of the surge in AI technology development and the ambiguity regarding the interpretation of software-related patents. The Federal Circuit has gradually refined the criteria for determining subject matter eligibility for software-related patents, and based in part on such jurisprudence, earlier this year the U.S. Patent and Trademark Office (USPTO) released revised guidance on examining patent subject matter eligibility under 35 U.S.C. §101. See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Considering the advances in AI technology and intellectual property law, how do these recent developments shape the outlook of AI patentability?

Securing patents for inventions that use artificial intelligence (AI) and machine learning can be challenging for innovators of these ground-breaking technologies, which attempt to use the processing power of computers to replicate the intelligence and learning capabilities of humans. Without patent or other intellectual property protection, they may be unable to commercialise their inventions, which could undermine investment in this dynamic field of research and development. To clear the way for innovators, the European Patent Office has recently amended its'Guidelines for Examination' by including a new section containing advice about how patents related to AI and machine learning technologies should be assessed. The guidance clarifies that whilst algorithms are regarded as'computational' and abstract in nature, which means they are not patentable per se, once applied to a technical problem they may become eligible for patent protection. Beneficially, the approach outlined in the guidance is similar to that currently used to assess the patentability of computer-implemented inventions.

In the coming years "artificial intelligence (AI) will become pervasive and touch every aspect of our lives and we'll wonder how we ever did without it", explained Sonia Cooper, senior patent attorney of IP policy and strategy at Microsoft. Cooper made her prediction at Technology Patent Network Europe in London today, June 21. Speaking on a panel titled "The Outlook for AI Patents", Cooper was keen to extinguish any concerns about the role of AI in the future. She said: "When we think about AI we perhaps think about robots taking over the world, or machines getting more and more intelligent and outperforming us. The reality, of course, is quite different."