WO2023156827 - ANOMALY DETECTION

National phase entry is expected:
Publication Number WO/2023/156827
Publication Date 24.08.2023
International Application No. PCT/IB2022/051475
International Filing Date 18.02.2022
Title **
[English] ANOMALY DETECTION
[French] DÉTECTION D'ANOMALIE
Applicants **
TELEFONAKTIEBOLAGET LM ERICSSON (PUBL) SE-164 83 Stockholm, SE
Inventors
HUANG, Zhaoji No.7 Longkouzhong Road, Tianhe District, Apt D2202 Guangzhou, Guangdong 950635, CN
ARAVAMUTHAN, Sarang 3029 Kilo Ave. San Jose, California 95124, US
BARRANCO, Angel 651 Pinelli Drive Los Banos, California 93635, US
DESHMUKH, Kunal Rajan 185 Estancia Dr., Unit 458 San Jose, California 95134, US
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Entry into National Phase under
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Quotation for National Phase entry

Country StagesTotal
China Filing1245
EPO Filing, Examination7067
Japan Filing591
South Korea Filing482
USA Filing, Examination3710
MasterCard Visa

Total: 13095

The term for entry into the National Phase has expired. This quotation is for informational purposes only

Abstract[English] A method (300) for anomaly detection. The method includes storing (s302) time series data, the stored time series data comprising a first set of N data points, wherein N > 2 and each data point in the first set of data points was obtained at a different point in time. The method further includes using (s304) the most current data point from the first set of N data points to determine whether or not to perform an anomaly detection process using at least N-1 of the N data points.[French] Procédé (300) de détection d'anomalie. Le procédé consiste à stocker (s302) des données de série chronologique, les données de série chronologique stockées comprenant un premier ensemble de N points de données, N étant supérieur à 2 et chaque point de données dans le premier ensemble de points de données ayant été obtenu à un instant différent. Le procédé consiste en outre à utiliser (s304) le point de données le plus actuel à partir du premier ensemble de N points de données afin de déterminer s'il convient ou non d'effectuer un processus de détection d'anomalie à l'aide d'au moins N-1 des N points de données.
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