GoNoGo

Can this company license this patent, for this use, in this place, for these years? Get an answer and the evidence behind it.

Explore the website · Try a clearance question · Watch the five-minute tour · Read the benchmark

GoNoGo is a patent-license clearance application built on NASA public data. It brings scattered records into a knowledge graph, checks a request against seven rules, and returns a verdict with the facts and source clauses that explain it. People approve new evidence; decisions keep enough context to be reproduced later.

Accuracy on 200 benchmark questions	False GO decisions in that benchmark	Median answer time	Model cost for those 200 questions
97.5%	0	1.557 seconds	$0.20

These are the recorded results for GoNoGo with its optional model reader enabled. The public demo uses a deterministic parser and makes no model calls. Results, method, and limits explain the distinction.

Start here: The problem → Ontology in plain words → The app → The engine → Governance → Results → Architecture → Run it.

The problem

A company wants to license a NASA invention. Before a licensing manager can say yes, five things need checking:

1. The patent: is it still in force?
2. Existing rights: does another signed license already cover the same use, territory, and dates?
3. Public notice: is a notice of intent still open for objections?
4. Export control: may the licensee's country receive the technology?
5. Ownership: has every co-owner consented?

The answers sit in different places: a patent list, legal-status records, notices, and agreement PDFs. A missed overlap can put two exclusive licenses on one invention. A missing ownership record can look like “no co-owners” unless the system preserves the difference between unknown and confirmed.

GoNoGo makes those checks explicit. A missing fact is never a yes.

Ontology in plain words

An ontology is a shared dictionary that also describes how things relate. Here, it defines a Patent, a Licensee, and a Grant. It says that a grant concerns a patent, names a licensee, and covers a use, a territory, and a period of time.

A knowledge graph holds the actual facts using those definitions. A few illustrative facts read like short sentences:

An executed grant → concerns → a patent
That grant        → names   → a licensee
That grant        → covers  → optics, in the United States, during its term
Optics            → includes → lasers

That last relationship matters. An exclusive grant for optics can overlap a request for lasers, even though the words differ. GoNoGo checks the hierarchy in both directions: a broad request can also overlap a narrower grant.

The explorer connects each class to its definition, the rules that use it, and the modeling decision behind it. Open the ontology explorer.

The few terms you need

Term	Plain meaning	Its job in GoNoGo
RDF	Facts written as subject → relationship → object	Connect patents, grants, parties, and evidence.
OWL	Machine-readable definitions and logic	Distinguish proposed from executed grants and derive rights coverage.
SKOS	Named concepts arranged into vocabularies	Give fields of use and territories stable meanings and broader/narrower relationships.
SHACL	Checks that data has the required shape	Reject malformed facts and evidence before approval. It does not decide clearance.
SPARQL	A language for querying an RDF graph	Retrieve the exact facts each rule needs using fixed templates.
PROV-O	A vocabulary for recording where things came from	Trace a fact to its source, proposal, approval, and responsible person.

The vocabulary is part of the decision: changing a hierarchy can change which rights overlap. It therefore goes through the same versioned release process as a rule change.

For a slower introduction, read Ontology from zero to a full build. For the exact model, see the ontology guide, the Turtle definitions, and the generated reference.

This is the beginning of the README. Read the rest at the original. Read the full README ↗