Alerta

SPATIOTEMPORAL TRANSFER MACHINE LEARNING

Resumen de: WO2025046310A1

A computer-implemented, machine learning method for spatiotemporal transfer learning. Sectors of an area are aggregated using preprocessed data from one or more data sources. The sectors are clustered based on different representations obtained for each context feature associated with each of the sectors. One or more context features that have a higher impact on a target feature to be predicted than other context features are identified from a plurality of context features and aggregated to obtain a representation of the area. Using the representation of the area, a particular sector within each of the clustered sectors is selected based on similarity to a respective centroid of the cluster to generate a set of particular sectors. A model associated with a source sector of the set of particular sectors is trained. The method has applications including, but not limited to smart cities, public safety and energy optimization.

MACHINE LEARNING MODEL POSITIONING PERFORMANCE MONITORING AND REPORTING

Resumen de: EP4773657A2

0001 Disclosed are techniques for wireless communication. In an aspect, a network entity receives a provide location information message from a user equipment (UE), the provide location information message including one or more positioning estimates derived by the UE during one or more positioning inference occasions of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more positioning inference occasions, and transmits a performance report indicating a performance of the machine learning model at least in deriving the one or more positioning estimates during the one or more positioning inference occasions.

TECHNIQUES FOR DETECTING EMERGING PATTERNS IN DATA USED FOR MACHINE LEARNING MODEL-BASED DECISIONS

Nº publicación: EP4773062A1 08/07/2026

Solicitante:

MICROSOFT TECHNOLOGY LICENSING LLC [US]
Microsoft Technology Licensing, LLC

Resumen de: EP4773062A1

0001 Described are examples for detecting emerging patterns in data. A detection system for detecting patterns outside of supervised machine learning models is provided for determining similarity scores between transactions to detect the emerging patterns. Transactions in the pattern can be reviewed to determine whether to render decisions on the transactions or similar subsequently occurring transactions. A self-correcting detection system is also provided for using machine learning models to correct for emerging patterns in the transaction data.