BlackTurtle understands what a technology does, linking tech that does the same job even across classifications. This is an investor overview; core algorithms are protected.
Why it is hard
A problem solved in turbine ceramics is needed in hydrogen storage — yet they never meet.
Half the world's patents are non-English. Korean patents are even more closed off.
Patents, papers, and government R&D don't talk to each other.
Engine
We collect and normalize Korean patents and national R&D — the Korean corpus that starts the moat.
We extract a "subject · action · object" function structure from each document — seeing tech by function, not class.
We embed body and "action/function" into separate semantic spaces so same-function tech sits close even when worded differently.
We fuse body, function and keyword signals and lift "same function, other field" candidates by classification (IPC).
Personal ontology + retrieved evidence produce an executable plan. Every claim carries a patent/R&D source.
Moat
Data (a once-closed Korean tech corpus), function-extraction know-how, and a personal ontology that compounds per user — three layers of moat that thicken over time.
Defensibility
Competitors may mimic outputs, but the time and know-how of extracting and refining function on a Korean tech corpus is hard to replicate.