How it works
Most legal AI wraps a general model in a nice interface and asks it to be a lawyer. NOA is built the other way around: a structured pipeline where each stage is separately testable, each recommendation is traceable to evidence in the record, and a dedicated verification layer checks the output before you see it.
Every profile NOA builds comes from primary source material: the actual file wrappers, the actual Office Actions, the actual outcomes. Nothing is inferred from summaries or scraped secondary databases.
When NOA tells you an examiner responds to a particular argument, that statement is anchored to specific prosecutions you can go read.
An allowance rate is a single number describing thousands of different situations. It tells you almost nothing about your case.
NOA models conditional behavior instead: given this rejection type, this technology area, this posture, and this stage of prosecution, what has this examiner actually done?
A firm's prosecution voice is not decoration. It is a set of repeated choices: how arguments open, how the record is characterized, how much is conceded, how amendments are framed.
NOA learns those choices from your own prosecution history and reproduces them inside your tenant, on your data, for your firm only.
Anyone can generate a document. The hard part is knowing whether it is filable.
NOA scores every draft against an explicit rubric derived from senior-practitioner review, and flags the specific failure modes that matter in prosecution.
Methodology
What NOA is not
NOA is a decision-support tool. A licensed practitioner reviews, edits, and signs everything.
NOA estimates likelihoods from historical behavior and shows you the evidence so you can disagree with it.
NOA replaces the blank page and the hours of file-wrapper reading nobody has time for.