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Artificial intelligence (AI) is rapidly transforming industries, and the field of intellectual property is no exception. Patent application drafting, traditionally a labor-intensive and detail-oriented process, has witnessed remarkable improvements with AI-powered tools. These technologies are helping patent professionals streamline workflows, enhance precision, and reduce costs. This comprehensive article explores the benefits, use cases, best practices, and tools for drafting patent applications with AI.

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.

In today's interconnected world, patent protection across multiple jurisdictions has become increasingly important for businesses and inventors. However, the complexity and cost of patent translations have long been a significant barrier to international patent protection. The emergence of artificial intelligence (AI) technologies is changing this landscape, offering new possibilities for faster, more accurate, and more cost-effective patent translations.

Patent prosecution, the complex process of securing intellectual property rights, often involves numerous rounds of correspondence with the patent office. Each Office action response, claim amendment, and negotiation stage creates a detailed record known as the patent prosecution history. Reviewing this history provides insights into a patent’s evolution, critical claim interpretations, and the legal boundaries established through prosecution history estoppel. Given the intricate nature of patent documents and the potential volume of Office actions for complex patents, the review process can be time-consuming and labor-intensive.

Artificial intelligence (AI) is transforming how we handle patent prosecution history analysis by automating key tasks, streamlining processes, and enhancing accuracy. In this article, we will explore the importance of reviewing patent prosecution histories, the benefits AI brings to this process, and how AI assists in claim interpretation, Office action responses, and broader prosecution management.

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.