WO2026033326 - CONSISTENCY VERIFICATION OF AIML POSITIONING CONFIGURATIONS

National phase entry is expected:
Publication Number WO/2026/033326
Publication Date 12.02.2026
International Application No. PCT/IB2025/057708
International Filing Date 30.07.2025
Title **
[English] CONSISTENCY VERIFICATION OF AIML POSITIONING CONFIGURATIONS
[French] VÉRIFICATION DE COHÉRENCE DE CONFIGURATIONS DE POSITIONNEMENT D'IA/ML
Applicants **
NOKIA TECHNOLOGIES OY
Inventors
LEE, Gilsoo
ASHRAF, Muhammad Ikram
CARRILLO MELGAREJO, Dick
MALAPATI RAVINDRAIAH, Deepa
Priority Data
202441060520   09.08.2024   IN
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Quotation for National Phase entry

Country StagesTotal
China Filing, Examination, Granting3525
EPO Filing, Examination, Granting39677
Japan Filing, Examination, Granting3578
South Korea Filing, Examination, Granting4832
USA Filing, Examination, Granting20540
MasterCard Visa
Total: 72,152
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Abstract[English] An apparatus including at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: transmit, to a network entity, a configuration used when at least one model is trained; wherein the at least one model is an artificial intelligence or machine learning model; and receive, from the network entity, information related to a consistency between the configuration used when the at least one model is trained and a configuration used when the at least one model is to be applied during inference.[French] L'invention concerne un appareil comprenant au moins un processeur ; et au moins une mémoire stockant des instructions qui, lorsqu'elles sont exécutées par le ou les processeurs, amènent l'appareil au moins à : transmettre, à une entité de réseau, une configuration utilisée lorsqu'au moins un modèle est entraîné, le ou les modèles étant un modèle d'intelligence artificielle ou d'apprentissage automatique ; et recevoir, en provenance de l'entité de réseau, des informations relatives à une cohérence entre la configuration utilisée lorsque le ou les modèles sont entraînés et une configuration utilisée lorsque le ou les modèles doivent être appliqués pendant l'inférence.

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