In the patent and innovation industry it is important to be updated on the same level with the emerging technologies and trends for both the companies and the innovators. Due to the large amounts of data involved this can become quite challenging and time consuming for most people. Patent data analysis and analysis on emerging trends have now become accessible and more organized with the help of Large Language Models (LLMs). LLMs like ChatGPT are a kind of generative AI that are trained on massive amounts of data from sources like the internet, books, articles and numerous forms of written content and in turn generates and manipulates text.
LLMs have a remarkable ability to read and understand language to the extent that they can reword, summarise, and deduce meaning from an existing text, produce reasonable responses to inquiries that an internet search could supply and are able to identify patterns and trends. In the realm of patents, the most common and important task is drafting patent applications, prosecution, specifications, and claims. Given the right set of prompts, the LLM tool can generate texts describing technology in the appropriate style, though the result is typically much shorter and less detailed than a normal application drafted by a patent attorney. The use of LLMs can also help applicants with very large patent portfolios to accelerate and lower the expense of their patent activity.
LIMITATIONS TO THE USE OF LLMs
LLMs are only helpful when given the points of argument in the form of prompts, only then can they produce lengthy argumentative texts. One of the concerns involving their use in preparing patent applications is the accuracy and completeness of the generated invention descriptions. This is because LLM tools are still flawed and certain errors and inaccuracy may be observed, though this can be improved over time.
On closer study, these writings will frequently appear to be exceedingly generic, and lacking in substance and quality, therefore such use is not without limitations. LLMs are indeed competent at summarising the general information, but these tasks are significantly distinct from the technically specific verbal reasoning needed for patent drafting and prosecution. The issue is that the current LLM tools used for such patent work still do not have a thorough understanding of the specialized technical sector, which prevents them from providing a more sophisticated response or fully capturing the creative concept of an invention.
Although the work produced by LLMs is currently of lower quality than that of a specialized patent attorney, but it may nevertheless be useful in specific industries or fields if LLMs can create work that is at least partially acceptable. A flood of AI-generated comments, drafts, or documents that meet patent standards may soon overwhelm patent offices. The use of LLMs may come with some risks such as a rise in expenses for applicants and the patent offices as well as a risk of a general decline in the quality of patent applications and responses.
Though LLMs have a potential to be revolutionary for the patent industry, they are still in their infancy and are not yet capable of replacing a competent patent attorney. While it is crucial to be aware of the potential risks of using LLMs in patent application preparation, it is also equally important to recognize their potential benefits. With the right approach, LLMs can be valuable tools in enhancing efficiency and productivity and it is most likely that AI tools will be considered to assist attorneys in future.