RealWorldAI.ai

TechTransfer.ai

From patent to license, with explainable AI doing the first pass

AI-assisted technology transfer with human review on the calls that matter

Patentability scoring and license term-sheet drafting that show their reasoning — so tech-transfer offices move faster without ceding judgment to a black box.

Evidence pending

Scope and decision boundary

Scope
Patentability-scoring intake and first-draft license term-sheet support.
Setting
University technology-transfer offices, startups, and industry licensing work.
Human-control boundary
Experts review consequential patentability and licensing calls; AI provides an explainable first pass.

The problem

Technology transfer is a bottleneck by nature: university tech-transfer offices, startups, and industry partners face more disclosures and deals than they can triage quickly, and the early steps — judging patentability, drafting a first term sheet — are slow, expert-dependent, and easy to fall behind on. Speed matters, but the decisions are consequential enough that an opaque "just trust the model" tool is a non-starter for institutions accountable to inventors and the public.

The approach

TechTransfer.ai provides a patentability-scoring intake and a license term-sheet generator, built around explainable recommendations and a responsible-AI governance layer. The AI handles the heavy first pass — structuring an intake, surfacing a patentability signal, drafting initial license terms — while exposing why it reached a recommendation so an expert can check it. Human review is required on consequential calls; the system accelerates the experts, it doesn't replace their judgment.

The outcome

Current component status, usage, turnaround time, and partner details are not established by an approved claim-level source.

Limitations and what remains unmeasured

The work is in pilot, and public component status or partner details are not confirmed on this page.

Not publicly reported: Usage, turnaround time, licensing outcomes, and partner results are not publicly reported here.

What actually works

For high-stakes expert work, the winning pattern isn't automation — it's an explainable first pass an expert can trust enough to build on, with a human firmly on the consequential decisions.

Sources and evidence

No claim-level source approved for publication.

Corrections

Corrections and material updates will be listed on this page. Use the correction request form to identify a disputed statement and its supporting source.

Most AI looks great on stage and breaks when it meets a real deadline. The useful question is what has been tested, what remains in development, and what people still decide.
Doug Liles
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