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. 

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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. 

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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 that we commonly hear from attorneys below.

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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.

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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. 

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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.  

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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. 

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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.

Recent comparative testing reveals that Solve Intelligence's Patent Drafting Copilot consistently outperforms GPT-4o in European patent claim drafting. Here we explore the technical and practical limitations of using GPT-4o for drafting claims suitable for European practice, and how using Solve’s Patent Drafting Copilot can deliver substantial improvements.

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Testing Methodology

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At Solve, we routinely run complex evaluations of our products and their ability to provide quality outputs against established benchmarks. This in turn allows us to iterate and improve on existing features and test new features, such that they perform to the high standard patent practitioners expect. 

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To illustrate the benefits of this approach, we conducted a test of our Patent Drafting Copilot, and specifically its ability to draft claims for a European patent application. We used the Patent Drafting Copilot to generate claims for the last 5 years of European Qualifying Examination (EQE) Paper A questions (2019-2024), and marked the independent claims according to the following criteria:

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• Clarity and conciseness
• Novelty and Inventive Step over the prior art provided in each paper
• Whether the essential features of the invention are included in the claims
• Whether unnecessary limitations have been avoided in the claims
• Compliance with European practice requirements

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The inputs we provided to the Patent Drafting Copilot included the invention disclosure and the prior art provided in each EQE paper, with no further instruction or input.

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For comparison, we provided GPT-4o with exactly the same information, as well as a simple prompt outlining the task of drafting European patent claims.

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Comparative Performance