Update README with patent details and method description (#274)

Author: MMathisLab

README.md

@@ -31,7 +31,7 @@
 
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-``cebra`` is a self-supervised method for non-linear clustering that allows for label-informed time series analysis.
+``cebra`` is a patented self-supervised method for non-linear clustering that allows for label-informed time series analysis.
 It can jointly use behavioral and neural data in a hypothesis- or discovery-driven manner to produce consistent, high-performance latent spaces. While it is not specific to neural and behavioral data, this is the first domain we used the tool in. This application case is to obtain a consistent representation of latent variables driving activity and behavior, improving decoding accuracy of behavioral variables over standard supervised learning, and obtaining embeddings which are robust to domain shifts.
 
 
@@ -50,6 +50,10 @@ Steffen Schneider, Rodrigo González Laiz, Anastasiia Filipova, Markus Frey, Mac
   [Learnable latent embeddings for joint behavioral and neural analysis.](https://arxiv.org/abs/2204.00673)
   Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis
 
+# Patent Information 
+
+- [Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings](https://patents.google.com/patent/US12499131B2/en).  Steffen Schneider* & Mackenzie Weygandt Mathis*. Awarded Dec 2025. Please contact the [TTO office](adam.swetloff@epfl.ch) at EPFL for licensing.  
+
 # License
 
 - Since version 0.4.0, CEBRA is open source software under an Apache 2.0 license.