ClaimHit does not collect AI outputs and rank them by confidence. Every result is evaluated across four independent factors before a risk level is assigned. A result only reaches HIGH when all four align.
When you submit a patent, ClaimHit runs nine frontier AI models simultaneously — Claude Sonnet, Claude Haiku, GPT-4o, GPT-4o Mini, Gemini 2.0 Flash, DeepSeek, Mistral, and Perplexity Sonar. Each model receives the same patent claims and searches independently without knowledge of what the others found.
A single model producing a confident result is hard to validate — confident hallucinations look identical to confident accurate results. Nine independent models converging on the same target is a qualitatively different signal. Independence is what makes the consensus meaningful.
Each factor captures a different dimension of infringement likelihood. All four must align for a HIGH result.
How many of the nine independent AI models flagged the same target. Each model searches without knowledge of the others — convergence across models from different companies with different training data is a strong signal. A target flagged by 7 out of 9 models independently is categorically more significant than one flagged by 2.
How many elements of the independent claim appear to be implemented — and which ones. Claim elements are not equal. Preamble language sets the context but doesn't define the invention. The novel core — the inventive steps — is where infringement actually turns. ClaimHit weights element matching toward the inventive steps, not the boilerplate.
The quality and specificity of citations underlying each match. A specific product datasheet URL with a version number and feature confirmation outweighs an inference from market position. HIGH risk requires at least three claim elements to have specific documented evidence — datasheets, standards clauses, FCC filings, developer documentation. Inference without documentation cannot produce a HIGH result.
Whether the accused product performs the same function, in substantially the same way, to achieve the same result as the claimed invention. This is the doctrine of equivalents test applied at the preliminary screening stage. A product that achieves the same outcome through a technically different route may still infringe — and ClaimHit tests for this explicitly rather than requiring literal feature-by-feature matching.
A noise penalty is applied when model agreement is high but documented evidence is thin. This filters a specific failure mode: many models agreeing that a company is relevant to a technology space without being able to find specific claim-element evidence.
Without this penalty, large companies in adjacent technology areas would consistently appear as HIGH risk — not because they infringe, but because AI models associate them with the technology. The noise penalty keeps HIGH meaningful.
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