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Idea: Platform for Patent Search
Chris Pondoc edited this page Jan 27, 2024
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NOTE: This is still very much a draft of the one-pager.
Create either a better platform for patent search or a middleware API for patent data
Tech and R&D teams spend a lot of time and money developing new products. However, if they donβt have the appropriate patent/the patent has already been made, then they will have wasted β or will waste β a bunch of dollars. In any case, the issue of searching patents to find one that is relevant to your potential intellectual property is a big problem.
- Patent data is non-standardized: there do exist some databases such as from the US PTO, but these are still hard to use, and most platforms (that are open access) still use a relational database and a basic SQL query
- Searching patents can be expensive: there are some existing players out there β albeit, I donβt think any of them are that big β but a lot of firms actually still go to patent search companies. These companies use a mix of automation + manual/expert due diligence in order to find the patents that are most relevant to a companyβs IP.
Thereβs a couple of ideas here, but overall, I think thereβs something here.
- Stripe for Patents: at the most basic level, given potential customers like patent search firms and companies building AI-powered patent search, we could just build the middleware for patents. This could involve scraping all of the existing patents from U.S. companies, cleaning them ourselves/automating that process, and running an API on a server that users can query.
- The Patent Search platform itself: in a more complex level, build all of the infra. This might be a crowded space, but I also donβt think itβs like AI/financial technology/SaaS, where there has been a winner take all (COULD BE VERY WRONG). At the very least, I also think there are a ton of interesting problems:
- HCI related to building a good search interface
- Data mining problems related to performing optimal search (do we give natural language queries? Do we use sliders and others? Etc.)
- Systems level problems related to surfacing results (how can we provide in-depth, comprehensive insights in as low latency as possible?)
- I think this is one of the first places where the idea is specific and the market is huge. I also think it doesnβt suffer from an industry like machine learning right now where thereβs a lot of hype around LLMs and ML infra but not too much signal on what the winners will be (i.e., there will have to be a graveyard of startups somewhere).
- We have the skillset to build this product. Yes, I think that the law space will be tricky β will have to think about selling into this space β but it seems like there is traction on this front and we could out execute. We clearly have the necessary technical skills, from the HCI knowledge, to the ML knowledge, to the core systems knowledge to make shit run fast.
- I also think we will enjoy these problems. Itβs not just doing full-stack stuff: we also get to be in the nitty gritty of solving a real-life problem but with cool tech and algorithms. -My dad works in the IP tech space, and can connect us with people. We can also get advisors on the tech side (i.e., SAIL people?) to folks on the law side (i.e., SLS?). Either way, I think we can leverage the Silicon Valley connections.
- What is the state of the law space right now? There might be a chance that this could be a multiple winners take all type of market β lots of law firms might have their own preference, and itβs more of a sales game. Nonetheless, weβd have superior product + eng quality, esp. in velocity (i.e., what fucking Citadel or Motional dev is gonna work on law tech?).
- Who do we sell to? In other words: what do we build? Thereβs clearly some low hanging fruit and some other cool opportunities here, but we want to think about where in this process the value accrues. Are AI-powered patent search platforms already big that we can actually do more of the data cleaning step? What about patent search firms β can they benefit from our tooling?
For other information, check out our team's Google Drive. For a daily stream of thoughts, check this document.
- OKRs and KPIs
- Team Coding Standards
- Real Customer Profile
- Launch Week Recap
- Unusual Academy Pitch Event
- Final Reflection
- 1/23 - Jay Borenstein
- 1/26 - Chris Oh
- 1/30 - Glenn Reid
- 1/30 - Adam Heher
- 1/31 - Samantha Phillips
- 1/31 - Chris Tsakalakis
- 2/2 - MZ Zaveri and Kasey Zhang
- 1/25 - Introduction
- 2/1 - Proposing a Product
- 2/13 - OKRs and KPIs
- 2/27 - Catching up for Last Weeks of Winter
- 3/5 - Demo + Discussion
- 4/2 - New Quarter
- Strategy for Search for Shopping
- Ideal Customer Profile Brainstorming
- Outreach Messaging
- Tracking Outreach
- Discovery Call Outline
- Feedback from Unusual on Slides
- Meeting with Seena from Nike
- Meeting with Mike from Launch
- Meeting with Heather from Walmart
- Meeting with Sandy from Walmart
- Demo Call Outline
- Prototype v0: A New Shopping Experience
- Prototype v1: Updated Bookworm for Demo Day
- Prototype v3: SUPost Battle for Software Fair