Law Professor · Legal AI Advisor · IP Attorney
Thomas Reichert
I use AI to study how the law actually works: large datasets of real decisions, models I fine‑tune myself, and results published in law journals. I help law firms and legal departments bring the same discipline into their own practice.
What Firms Book
- What AI Reveals About How the Law Actually WorksKeynote or CLE
- Building Legal AI You Can DefendEthics CLE
- From Pilot to PracticeLeadership briefing
- Four Controls That Make Legal AI DefensibleAdvisory method
Recent talks Stanford Law School (IP Scholars Conference) · Northwestern Pritzker School of Law · Southeastern Association of Law Schools · Lawyers Association of Kansas City · Jackson County Bar Association CLE All talks →
Featured Research
Doctrine, Data, and the Death of DuPont
I used large language models to code roughly 4,000 decisions of the Trademark Trial and Appeal Board. Two of the thirteen DuPont factors, mark similarity and goods relatedness, predict outcomes with 99.55% accuracy.
The Fair Use Database
Codes every substantive federal fair use opinion, with pin-cited court language. Its research assistant, Folsom, answers only from the coded corpus and cites the evidence behind every answer. The coding is validated against 453 human-coded analyses.
Coverage of the Research
This paper could be very useful... Empirical support for John Welch's mantra, which turns out to be understated: mark and goods don't predict 95% of the outcomes of 2(d) appeals to the TTAB, they predict 99%!
Rebecca Tushnet, Frank Stanton Professor of the First Amendment, Harvard Law School, 43(B)log
The 13-factor DuPont analysis to determine likelihood of confusion is not as complex as it appears when practicing before the Trademark Trial and Appeal Board... Just two of the factors can be used to predict whether the board finds challenged marks confusing 99.57 percent of the time.
MLex, “Only 2 of 13 DuPont Factors Decide Nearly All US TTAB Cases, Research Suggests” (Jan. 9, 2026)
MarkSense Analytics. A scoring platform built from my TTAB research data. It scores trademark similarity and likelihood of confusion against decided cases. About MarkSense →
Planning a CLE, Retreat, or AI Rollout?
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