Automated real time anomaly detection for networks: Page 2 of 2

September 14, 2020 //By Jean-Pierre Joosting
Anritsu patent for eoMind anomaly detection algorithm
Anritsu has received a patent for its eoMind anomaly detection algorithm based on streaming analytics and machine learning that automatically identifies issues in real time within a telecom network.

With the first deployment in 2016, Anritsu has built upon their experiences with customers to have the leading streaming analytics system in the market that uses machine learning and automation to address customer experience and service issues in real time. eoMind is cloud-ready, lightweight and easy to use. It detects anomalies out-of-the-box, delivering immediate value across all technologies including 3G, 4G, IMS, VoLTE and 5G. Issues are detected faster via subscriber and service anomalies. eoMind is vendor independent and works on data from any source be it Anritsu or 3rd-parties.

With eoMind, operators are achieving time savings of up to 50% in MTTR (Mean Time to Resolve), huge cost savings and improved operational efficiency. Proactively fixing issues means reductions in the number of overall affected subscribers, with up to 30% fewer subscribers affected, leading to fewer calls to customer care, fewer escalations and improved customer experience and retention.

The patent "Systems and methods for measuring effective customer impact of network problems in real-time using streaming analytics" with number US PATENT 10,686,681 was granted to Anritsu with co-inventor Davide Motta.

www.anritsu.com

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