Machine learning-based troubleshooting of VoLTE calls

US10339456B2 · kind B2 · utility

Assignee

• T-Mobile USA, Inc. · US

Inventors

• Jie Hui · Mercer Island, US
• Yunhan Jia · Sunnyvale, US
• Yihua Guo · Ann Arbor, US
• Alex Yoon · Plano, US
• Antoine T. Tran · Bellevue, US
• Kranthi Sontineni · Renton, US
• Z. Morley Mao · Ann Arbor, US

Key dates

• Filing date: May 4, 2017
• Grant date: Jul 2, 2019
• Expiry date: May 4, 2037

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04M2207/185
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A VoLTE diagnostic engine may receive VoLTE call records of VoLTE calls that are carried by a wireless carrier network for multiple user devices. The VoLTE call records may include performance indicators and call features for the voice calls. Each call feature of a VoLTE call may represent a circumstance under which the VoLTE call is established and ended. The VoLTE diagnostic engine may apply a decision tree to the VoLTE call records to identify critical features of one or more call conditions that lead to Quality of Experience (QoE) problems for the VoLTE calls captured in the VoLTE call records. Each call condition may include a subset of the call features. Further, the VoLTE diagnostic engine may apply a classifier on the critical features to determine a root cause of a QoE problem for at least one call conditions.