ClaimHit doesn't score infringement — it surfaces candidates that may infringe. Every patent runs through a five-stage pipeline that adds evidence at each step. Candidates confirmed on a manufacturer's own page surface as Good Match. Candidates the models agreed on but couldn't be independently confirmed surface as Possible Match.
When you submit a patent, ClaimHit runs six models in parallel — multiple frontier models from different providers, including several run in distinct configurations. Each model receives the same patent claims and independently proposes candidates, with no knowledge of what the others have found. This is the discovery step; everything they propose is verified in later stages.
A single model producing a confident result is hard to validate — confident hallucinations look identical to confident accurate results. Multiple independent models converging on the same target is a qualitatively different signal. Independence is what makes the consensus meaningful, and where models disagree we still keep candidates so the next four stages can verify them against the open web.
Each stage either adds evidence or removes noise. Candidates that survive all five and confirm on a manufacturer's page surface as Good Match. Candidates the models agreed on but couldn't be independently confirmed surface as Possible Match.
Six models run in parallel against your patent’s inventive contribution. Each independently proposes candidate products and companies. Where multiple models agree, we have a strong consensus signal. Where they don’t, we still keep the candidates and verify them in later stages — disagreement isn’t a reason to drop a candidate, only a reason to insist on independent evidence.
In parallel with the models, we run a six-slot web search across two complementary retrieval engines — one optimized for semantic relevance (matches concepts even when keywords differ), the other for keyword precision against Google’s index. Each slot targets a different page type: manufacturer marketing language, feature pages, use-case explainers, competitive comparisons, end-user reviews, and an invention-specific angle. This catches real products the models missed.
Before running expensive verification, we drop entries that pattern-match content sites — UGC platforms, blog and news subdomains, editorial paths, patent corpus sites, academic aggregators. These are correctly classified as non-products by later stages anyway, but filtering them upstream saves analysis time without losing signal. The filter is conservative: anything ambiguous passes through.
Each candidate goes through a focused model review that asks one question: is this product in the same category as the invention? Solid-state sensor pages get dropped from rotating-sensor searches. Mapping software gets dropped from sensor-hardware searches. The check is recall-tuned — borderline products pass through to verification rather than being dropped early. Decisions come back with a confidence band that affects the final ranking.
For candidates that pass category-fit, we attempt to confirm them on the manufacturer’s own page. We check that the URL resolves, that page content matches patent-distinctive vocabulary, and that a final language-model pass confirms the named product is actually hosted on that domain. Candidates that confirm get tagged Good Match. Candidates the models agreed on but we couldn’t independently confirm get tagged Possible Match — both surface in your results.
Every result is sorted into one of two buckets, by what we could verify.
Search your patent in about 90 seconds.
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