Harry Bedford

The UK Supreme Court has granted permission for Emotional Perception AI to appeal the decision of the Court of Appeal of 19 July 2024, in the latest chapter in this potentially pivotal case addressing the patentability of computer-implemented inventions, specifically those involving artificial neural networks (ANNs).

Generative AI is already changing how patent attorneys work, and those that use AI platforms, such as Solve Intelligence’s Patent Copilot, are reporting efficiency gains and higher quality outputs.

As the uptake of generative AI increases amongst patent attorneys, professional bodies are starting to consider best-practice and standards for using this technology. 

The European Patent Institute (epi) recently released comprehensive guidelines detailing how patent attorneys can responsibly integrate AI into their workflows. These guidelines emphasise ethics, professionalism, and accountability when using AI. In this article, we set out the key takeaways of the guidelines, and how, at Solve Intelligence, we empower attorneys to use AI in line with these guidelines, ensuring that our Patent Copilot remains a powerful but controllable tool for assisting in patent practice.

Patent attorneys often face high workloads, tight deadlines, and the pressure to deliver impeccable work. AI-powered tools like Solve Intelligence’s Patent Copilot are revolutionising how patent applications are drafted and prosecuted, offering attorneys significant efficiency gains. Here’s how to make the most of these tools and transform your workflow.

Drafting patent applications can be an arduous task. The process often requires several interactions with inventors to extract enough information to start the process, and then begins the exercise of turning that information into a coherent and detailed ~30 page patent specification. This of course takes time (and sometimes more than the proposed/estimated fee for the draft would allow). For this reason, patent drafting is sometimes considered a loss leader by those in private practice. 

From another perspective, the cost of engaging a patent attorney to draft a patent application can be a huge barrier to the uptake of IP, particularly for start-ups and SMEs. Registered IP rights can mean a great deal to these types of applicants, both in terms of carving out a slice of the market and attractiveness to potential investors. Drafting patent applications has thus historically presented a ‘problem’ to both patent attorneys and their would-be clients. 

Artificial intelligence (AI) is changing the game in this regard. Forward-thinking attorneys that have already started utilising AI, and those using Solve Intelligence’s Patent Drafting Copilot have reported considerable improvements in their drafting practices. Such improvements are capable of tackling the drafting problem, providing benefits to both attorneys and their clients or companies alike. We’ve highlighted some of these improvements below.

Earlier this year, the EPO introduced new guidelines for examination relating to inventions concerning artificial intelligence (See G-II-3.3.1). The last paragraph of these guidelines suggest that applications to AI-related inventions may require specific disclosure surrounding any algorithms used by an AI invention, as well as any training data used to train the AI, where such training data is required to achieve the technical effect of the invention. 

A change in the Guidelines usually reflects a change in thinking or application of the law by the EPO. Indeed, it’s always interesting to see how such changes are actually implemented in practice.  

The recently issued decision T1669/21 of the EPO Board of Appeal provides useful insight into exactly what sorts of specific disclosure may be required to satisfy the sufficiency requirements for patent applications relating to AI inventions. 

The unprecedented scale of content generated by AI presents an interesting challenge, in terms of prior art, to established patent law practice. With AI capable of autonomously producing technical content, future patent applications may face an unprecedented wave of AI-generated prior art disclosures that could be relevant in the assessment of novelty and nonobviousness (or inventive step). This article explores how AI-generated content may reshape how we consider prior art, and what might need to be done, as we navigate a new era of innovation influenced by machine-generated insights.