The product is the data, and the data is only as good as the calibration. On February 16, 2021, LeoLabs was granted US10921427B2, “Drone-based calibration of a phased array radar,” classified in G01S 7/4052, the radar-calibration art. The claim uses a drone carrying a known reference to calibrate a ground radar.
LeoLabs is a private firm that sells commercial space-situational-awareness data — the positions and conjunction risks of objects in low earth orbit — to satellite operators and governments. There is no SEC filing to read here; the business case lives in the public record and the patent moat. And the moat is accuracy: customers pay for collision warnings only if they trust the underlying measurements.
Calibrating a large phased-array radar is normally slow and expensive. A drone-based method that automates it lowers the cost of keeping each radar accurate and lets the network scale to more sites without a matching jump in maintenance labor. For a subscription data business, that is the cost side of the model — cheaper, more reliable calibration means more trustworthy data produced at lower cost, which is the whole game.
The honest caveat: a calibration patent supports the data product but does not, by itself, prove the subscription economics or the size of the SSA market. Accuracy is necessary, not sufficient.
Still, it is the right place to look for the moat. When a private SSA company patents around making its sensors more accurate and cheaper to maintain, it is investing in the one thing a data subscription is actually sold on: that the numbers can be trusted.