Alerta

PREDICTION OF EQUIPMENT-RELATED EVENTS USING MACHINE LEARNING

Absstract of: US2025370446A1

Techniques are disclosed herein for machine-learning (ML)-assisted event prediction for industrial machines. A first set of embeddings can be generated based on labeled first event data, which can be labeled with classifiers determined based on signaling channel information for the first event data. A neural network can be trained, using the classifiers, to generate (i) a similarity score for the first set of embeddings and the second set of embeddings and (ii) a classifier recommendation for the second set of embeddings. The second set of embeddings can be generated based on data collected using condition monitoring sensors for a particular industrial machine. Accordingly, the system can generate alerts, recommendations, and/or notifications based on the automatically classified data encoded in the second set of embeddings. Incremental training techniques are disclosed for further training the neural network to minimize false positives and/or false negatives.

ANALYSIS AND CORRECTION OF SUPPLY CHAIN DESIGN THROUGH MACHINE LEARNING

Absstract of: US2025371491A1

A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

METHODS AND APPARATUS FOR MACHINE LEARNING BASED MALWARE DETECTION

Absstract of: US2025371152A1

Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

METHOD FOR ADJUSTING HYPERPARAMETERS OF A MACHINE LEARNING MODEL

Nº publicación: EP4657257A1 03/12/2025

Applicant:

ST MICROELECTRONICS INT NV [CH]
STMicroelectronics International N.V

Absstract of: EP4657257A1

Selon un aspect, il est proposé un procédé mis en œuvre par un système informatique (SYS) d'hyperparamètres d'un modèle d'apprentissage automatique, le système informatique (SYS) comportant une unité de traitement (UT) configurée pour exécuter plusieurs processus en parallèle, le procédé comprenant une exécution de plusieurs méthodes de recherche indépendante d'hyperparamètres dans différents processus parallèles de l'unité de traitement (UT), les résultats des tests des combinaisons d'hyperparamètres étant stockés dans une mémoire du système informatique partagée entre les différents processus, et dans lequel chaque processus évalue si une combinaison d'hyperparamètres recherchée a déjà été testée par un autre processus à partir des résultats de tests stockés en mémoire, et prend en compte, dans son propre historique de tests, les résultats de tests stockés dans la mémoire si la combinaison d'hyperparamètres recherchée a déjà été testée.