The Potential of Large Language Models in Patent Application Preparation: A Rebuttal

Ian Schick
3 August 2023

The advent of Large Language Models (LLMs) has sparked a debate in the legal sphere, particularly in patent application preparation. This debate is fueled by rapid advancements in artificial intelligence and machine learning, which are transforming various industries, including the legal profession. The potential of LLMs in automating and enhancing legal processes, such as patent application drafting, is immense and cannot be overlooked.    

While some express concerns about using LLMs, it is essential to consider the potential benefits and transformative impact these models can bring to the legal profession. These concerns often stem from a lack of understanding or fear of the unknown. However, when we delve deeper into the capabilities of LLMs, we can see that they offer numerous benefits, such as increased efficiency, accuracy, and consistency in preparing patent applications.    

The primary concern raised is the potential disclosure of inventions to LLMs, which some allege could be considered a third-party disclosure. This concern arises from the misconception that LLMs function like traditional search engines or databases that store and retrieve information. However, this is not the case. Many LLMs are designed with privacy and confidentiality in mind. For instance, OpenAI’s GPT4, a popular LLM, can be configured so that chat history is disabled, which means prompts and responses will not be used to train the model further. Conversations are retained for 30 days and reviewed only when needed to monitor for abuse (e.g., hate speech) before permanently deleting. This feature ensures that the information fed into the model for generating content is not stored or used for any other purpose. Therefore, the risk of disclosing confidential information is significantly mitigated.    

Moreover, the argument that using an LLM may start the 12-month grace period for filing an application in the U.S. or prevent an application from being filed in any country that requires absolute novelty is not entirely accurate. This argument is based on the assumption that using an LLM equates to a public disclosure of the invention. However, this is a misunderstanding of how LLMs work. The use of LLMs in creating content for patent applications does not equate to public disclosure of the invention. An LLM is a tool used by an individual, not a public platform. It is akin to using a word processor or a legal research tool, which does not constitute public disclosure.    

Another concern is the accuracy and completeness of LLMs’ invention descriptions. This concern is valid as LLMs, like any other tool, are not perfect and can make mistakes. However, it is important to remember that these models are designed to learn and improve over time. While it is true that LLMs can occasionally generate inaccurate information, it is also true for human attorneys who are not infallible. Humans, too, can make errors, overlook details, or misinterpret information. The key is to use LLMs to assist in preparation, not as a replacement for human oversight and expertise.    

The argument that using an LLM may result in a patent application of lower quality than the manual work product of the lawyer is subjective. This argument seems to underestimate the capabilities of LLMs and overestimate the consistency of human performance. The quality of a patent application depends on various factors, including the complexity of the invention, the attorney's expertise, and the clarity of the description. Adhering to commonly accepted best practices, a well-designed LLM can provide a solid patent application draft, which can then be reviewed, amended, and validated by an attorney. This collaborative approach can lead to a high-quality patent application that effectively protects the invention.    

The claim that LLMs are frequently “out of date” is also debatable. This claim seems to overlook the dynamic nature of LLMs and their ability to learn and adapt. While it is true that LLMs are trained on data that is 1-2 years old, they are continually updated and improved. This continuous learning and updating ensure that the models stay relevant and effective. Furthermore, the vast amount of data they are trained on often includes timeless principles and concepts that remain relevant despite the passage of time.    

In conclusion, while it is crucial to be aware of the potential pitfalls of leveraging LLMs in patent application preparation, it is equally important to recognize their potential benefits. It is about striking a balance between taking advantage of the benefits of LLMs and mitigating their limitations. With the right approach, LLMs can be a valuable tool in the patent procurement pipeline, enhancing efficiency and productivity. As with any tool, the key lies in how it is used. It is about using the tool wisely and effectively to achieve the desired outcome.

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