patentml

Machine learning from expired patents. Zero dependencies. Pure Python stdlib.

37 modules, 131 classes and functions — every algorithm reconstructed from a United States patent that has expired into the public domain. The patents that built modern ML were filed by IBM, Bell Labs, Microsoft Research, Lockheed, AT&T and Lucent between 1995 and 2006. They have all expired. This library is what they describe, as clean modern Python, with no imports beyond the standard library.

pip install patentml

No numpy. No scipy. No compiled extensions. If it runs Python 3.8+, it runs patentml — locked-down corporate machines, serverless functions, air-gapped environments, Pyodide in the browser, and (with light trimming) MicroPython boards.

Quick start

from patentml import RandomForest, ScalableKMeans, ThompsonSampling, KalmanFilter

# Classification — US6816847 (Microsoft, 1999)
forest = RandomForest(n_trees=25)
forest.fit(X_train, y_train)
labels = [forest.predict(x) for x in X_test]

# Clustering — US6012058 (Microsoft, 1998)
km = ScalableKMeans(k=3)
km.fit(points)

# Bandits — US6981040 (Utopy, 2000) [919 forward citations]
bandit = ThompsonSampling(n_arms=4)
arm = bandit.select()
bandit.update(arm, reward=1.0)

# State estimation — US6795794 (Univ. Illinois, 2002)
kf = KalmanFilter(dim_state=2, dim_obs=1)

What's inside

Provenance

Every module documents its source patent: number, assignee, filing year, and forward-citation count. Highlights:

The full list of ~40 source patents is in the package docstring: python -c "import patentml; print(patentml.__doc__)".

All source patents are expired. The implementations are original code, MIT licensed.

Why

Modern ML stacks are heavy, opaque, and supply-chain risky. Sometimes you need one algorithm — a Kalman filter on a microcontroller, a bandit in a serverless function, k-means in a browser — without 200 MB of compiled wheels. And sometimes you want code you can actually read: every module here is a single self-contained file you can audit in one sitting.

These algorithms earned their citations the hard way. They still work.