Jun 25th, 2023

AI-Powered IP/Patent Reports and Analysis

Inspiration

We run a startup and have a consistent problem of coming up with new ideas and wondering if anyone has ever built this before and if the idea already has already been patented. This leads to hours of searching around the internet and various patent databases to see what's out there when we could spend this time building new features, finding customers, and making money for our business.

This is undoubtedly a process that costs businesses a lot of money, in fact according to UpCouncil, each patent search can cost between $100 to $3000, depending on the complexity. If we look at a product like the recently-announced Apple Vision Pro, 5,000 patents were filed for this one product. Assuming each of these patents were extremely advanced due to their deeply technical nature, patent searches for the Vision Pro cost Apple $15,000,000. This might be a drop in the bucket for Apple, but for startups and much smaller companies, this is simply unattainable and might lead to valuable IP being left unprotected. We knew this was a problem that we had to solve, both for ourselves and other entrepreneurs like us.

What it does

IdeaSleuth generates an Intellectual Property/Existing Patent Briefing based on an idea that you have. Our project takes a description, finds, and reads patents from across the globe that are relevant (in any language!), and generates a PDF with the related patents, a detailed analysis of the IP landscape, suggestions to improve your idea, and even a score or how patentable your idea is!

How we built it

IdeaSleuth takes a description of an idea from the user and uses an LLM-powered agent (built with Langchain) to take this description and convert it into a series of SQL queries which is used to search the BigQuery database of international patents. Once relevant patents have been found, IdeaSleuth scrapes the Google Patent page for the patent in order to get the PDF, and loads all of the PDFs into our Pinecone vector database. From here, we use a GPT-4 agent to run a similarity search on our Pinecone database and answer the pre-set selection of questions, as well as assign the idea on a rating of how patentable it is. Once all of this information has been written, we use the reportlab python library to generate a stylish PDF which makes it easy to quickly consume all of the analysis about your idea and relevant IP. The front end for the application is built with React and hosted on Vercel.

Screenshots