http://ipkitten.blogspot.com/2025/02/review-of-ai-patent-drafting-software.html
LLMs will undeniably have a major impact on the legal industry, but are any of these tools suitable for the pharma and biotech industry? PatKat has decided to take a look to see if any of currently available tools can assist with patent drafting for life sciences. As a caveat, these reviews will focus on the technical capabilities of the software and not the pricing or security features of the tools.
LLMs for patent drafting
We are now awash with companies claiming to provide LLM software capable of drafting patent applications. Whilst there are already a number of blogs out there reviewing the different companies, for this Kat, patent drafting is a subject-specific activity. Patents in the life sciences field are very different to those in the mechanical and software fields. The process of drafting patents in life sciences is correspondingly also very different. In life sciences the focus is very much on the data and how this supports the claims. The claims in life sciences require very domain-specific details about the drug modality, such as sequence information and chemical structure. So how do the existing AI software tools cope with biotech and pharma inventions? The first tool put through its paces by IPKat is the AI patent drafting software from Qatent (now part of Questel).
| Mechanical and life sciences patent drafting: Worlds apart? |
Qatent’s patent drafting software
- A “hybrid AI Model” that allows user input from the patent attorney into what the LLM is doing.
- A “paraphrasing tool” that promises to “give your patent application the correct scope by choosing among different variations of the same sentence/phrasing.”
- A definitions tool that detects technical terms in the text and proposes alternative definitions, derived from Qatent’s “extensive terminology database”.
- “Relation tables” that “keep close tabs on how your claims express the relation between terms. Find typical errors, missing links, etc.”
- A Diagrams generator, with the current offering producing “simple step diagrams”.
The Qatent tool user interface is very simple and easy to use. The claim and description drafting and editing happens within a browser to which you can upload documents of various types. The finished claims and/or complete application can then be exported (clean or with mark-up) in PDF or docx format. This flexibility is appreciated. Overall the user experience is simple and pain-free (not always the case with new software!).
The Qatent tool provides two workflow options. The user can either upload an Invention Disclosure for drafting of the claims and description, or the user can upload their own claims. The optional workflow is a good feature, allowing the user to draft their own claims and use the tool for drafting the description.
Multiple types of document can be uploaded to the Invention Disclosure, including PDFs. It is also possible to copy and paste plain text into an Invention Disclosure dialogue box. If the user is uploading their own claims, these can also be pasted into a dialogue box or uploaded.
In a potentially nifty feature, the tool will automatically extract figures from the uploaded document. However, in this Kat’s test example, in which the input was a scientific paper in PDF format, the figures appeared as blank black squares. Presumably, a simpler image format is needed. There is also the option of just uploading the figures separately.
The AI outputs can be edited within the tool itself, and then exported in PDF or docx format (with or without the notes provided by the tool).
Claim drafting based on data
The first test example this Kat tried was a dose invention for a monoclonal antibody. To test the AI claim drafting software, the data uploaded was a scientific publication describing in detail the identification of the optimal IV dose of a monoclonal antibody for a phase 3 clinical data based on data from a phase 2 clinical trial. The target claim was therefore a method of treatment claim for treating the relevant indication in the subject, comprising administering the antibody to the subject at the identified optimal dose.
In the first test, only the data document was uploaded without any additional description of the invention. Without the context of an invention disclosure the software clearly struggled to identify the invention. The first claims suggested by the AI software, for example, included a method for determining an optimal dose of the drug, as opposed to the more appropriate claim for method of treatment comprising administering the optimal dose of the drug.
The tool also hallucinated incorrect subject matter for claims. The software suggested, for example, claims for a random drug combination that was not mentioned in the data, a whole list of odd antigens for the antibody (despite the data being very clear on the identify of the antigen), and suggested some claims that assumed the monoclonal antibody was a small molecule (e.g. by suggesting the monoclonal antibody “or its salt”). The target and identity of the drug as a monoclonal antibody was very clear from the data, so it is unclear why the software chose to ignore this information. The software also failed to suggest any claims in different formats that are relevant to this type of invention, e.g. EPC 2000 second medical use.
Claim drafting based on data and the Invention Disclosure
In a second test, the same data was uploaded, but this time with a brief Invention Disclosure describing the invention as the optimal IV dose of the monoclonal antibody. The Invention Disclosure specified the name of the drug, the amount and frequency of the optimal dose, and the disease to be treated.
The software performed much better having been given this additional information. The first claim and the first 3 dependent claims were passable method of treatment claims, indicating the correct drug, indication and optimal dose, as identified in the invention disclosure. However, the subsequent dependent claims descended into random features that made little or no sense based on the data.
The claim dependencies also needed work. The developers have clearly tried to avoid incorrect dependencies in the claims, and lack of antecedent basis, by telling the tool to limit the use of multiple dependencies. Most of the claims thus depended only on the first independent claim. In terms of ensuring sufficient basis for all combinations of features, this is less than ideal. The structure of the claims was also odd, with claims dependent only on independent claim 1 alternating with claims dependent only on independent claim 2.
Claim drafting: Conclusion
The major issue with claim drafting was that the Qatent software does not understand different categories of pharmaceutical invention or drug modalities. Overall, the Qatent tool is not fit for drafting claims for this type of invention. It is possible that crafting a highly detailed invention disclosure might have improved the situation. However, the data uploaded was sufficiently detailed such that the invention should have been easy to understand and identify. It is therefore not clear that writing a detailed invention disclosure would have helped. Also, by the time one has finished optimising the invention disclosure, one might as well have either just manually drafted the claims or used a LLM model directly.
Description drafting from the Claim
One of the more tedious tasks of patent drafting is converting and expanding the claims into appropriate basis. To this Kat, this process is definitely long-hanging fruit for automation with AI. So how did the Qatent tool perform?
To test this functionality, this Kat uploaded her own drafted claims for the dose invention used in the previous test, together with the data and the invention disclosure.
The basis section provided by the software repeated the text of each claim in the appropriate format for the description. However sometimes crucial information was lost in the transfer. For example, for the first claim, the embodiment was described nonsensically as being the antibody “administered at a dose of about mg every weeks”. This unfortunately does not inspire confidence.
For each claim, the tool also suggested some text describing the advantages of the features in the claims. Some of these were helpful. However, despite the data source document providing detailed information on the advantages of the invention, the majority of these suggestions were very high-level and vague, providing little value. The extensive definitions that would be normally expected in a patent application relating monoclonal antibody for a therapeutic use were lacking.
Technical field and background information
In a patent specification, the description will often include a short paragraph on the technical field. The Technical Field paragraph provided by the tool was clearly taken from the user inputted International Classification number and was therefore too broad. For example, for the monoclonal antibody dose invention the technical field was said to include “chemical compounds or medicinal preparations and preparations for medical, dental or toiletry purposes”. The “Background Art” section generated by the tool was also very short, around 1 paragraph. The Background also appeared to be less of an introduction, and more or a description of the invention.
Final document format
After you have finished editing the draft in the Qatent tool, you can export it in word of PDF format. The exported word document had line numbering headings, but no other word formatting, e.g. no bold headings, no use of word Styles for headings for easy navigation and formatting, and the claims were not a numbered list. This made making further edits to the document rather cumbersome.
Final thoughts
Qatent’s AI patent drafting tool shows promise with its user-friendly interface and flexible workflow options. However, the current offering falls significantly short when handling specialised life sciences patents. While the tool performed better with explicit invention disclosure information, the software still struggled with understanding pharmaceutical invention categories and drug modalities, leading to hallucinated dependent claims and missing critical formats relevant to biotech applications. The description drafting functionality suggested only vague advantages for the claimed features and lacked the specialised definitions crucial for pharmaceutical patents. The final document formatting also requires substantial manual refinement. For patent professionals in the pharma and biotech sectors, the Qatent software currently appears to be a supplementary tool at best, requiring significant human oversight and expertise to produce acceptable patent applications in these specialised fields.
However, for this Kat, the inadequacies of the Qatent tools relate more to the particular requirements of life sciences patent drafting as opposed to the overall capabilities of the tool itself. Qatent was developed, like so many of the AI-patent software tools, by AI developers and patent attorneys with a background in software and mechanical drafting. It is therefore not surprising that the resulting software does not cope well with biotech or pharma drafting, which requires highly specialised field-specific expertise and an entirely different approach to mechanical or software patent drafting.
The search for AI patent drafting software for life sciences continues! Do readers have any suggestions or recommendations of AI tools for pharma and biotech patent drafting?
Further reading
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