Uncovering Promising Patents and Research with AI

Hacker News

We're excited to share a tool that uses AI to scan patents and research highlighting the most promising ones for investors to consider. How it works: 1) Setup: Investors start by inputting their investment strategies or target criteria. 2) Scanning: Our system then scans patents and academic research hourly, ensuring you never miss out on a potentially groundbreaking invention. 3) Scoring: Each finding is meticulously scored, focusing on its commercial potential, viability, and relevance to your goals. 4) Summary: The top picks are analyzed further, with alerts and detailed reports sent directly to you. The platform is tailored for investors who aim to identify promising research and patents before others. If you're interested in gaining an edge in your investment strategy, we invite you to request access. We're here to answer any questions.

Visit product View on Hacker NewsFeb 21, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

37%37% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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