T-mobile Usa, Inc.

Kranthi Sontineni

Jie Hui

Yunhan Jia

Zachary William McPherson

Kevin Lau

Peter P Myron

Jeffery A Smith

Himesh Bagley

Jun Liu

Antoine T Tran

Jong Sung Yoon

Samson Kim-Sun Kwong

Karthik Iyer

Suchit Satpathy

Samson Kwong

Qi Alfred Chen

Z Morley Mao

Yihua Guo

Alex Yoon

Haokun Luo

Zachary Mcpherson

The techniques described herein involve analysis of communication data included in trace file(s) of device(s) involved in a communication. These trace file(s) may each include data associated with multiple layers of a communication protocol stack of a respective device or data associated with a single such layer. The techniques may further involve one or more of determination of performance metrics associated with data at a specific layer of a specific device, correlation of the data between layers of a device, or correlation of data across multiple device(s) involved in the communication. The performance metrics or correlated data may then be analyzed based on thresholds or models to determine whether the performance metrics or correlated data exhibits a degraded quality of user experience. Also or instead, graphic or textual representations of the performance metrics or correlated data may be generated.

Quality of user experience analysis

Described herein is a network device configured to determine and provide alerts of communication link failures across layers of a communication stack of the network device. The network device determines at a radio link layer of the communication stack that a communication link between the network device and a network has failed. The network device then alerts one or more components associated with another layer of the communication stack of the determination that the communication link has failed.

Cross-layer link failure alerts

The remote analysis of mobile device quality of experience diagnostic files includes analyzing diagnostic files. A diagnostic driver application resident at the mobile device is remotely activated to generate and send diagnostic files to one or more network resident servers for analysis. The diagnostic files may be analyzed to determine the mobile device quality of experience, and to determine a root cause and geographic and/or network location of a problem, such as dropped calls or poor data connectivity. In some embodiments, the diagnostic files may be aggregated to form a database of aggregated diagnostics, which can be used to further analyze a telecommunications network to determine the root cause of a network problem.

Remote driving of mobile device diagnostic applications

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.

Machine learning-based troubleshooting of VoLTE calls

The techniques described herein involve analysis of client device Quality of Experience diagnostic files including an operations log or diagnostic files for a client device. The client device Quality of Experience diagnostic files may be generated by a client device and sent to a network node for analysis. The diagnostic files may be analyzed to determine device Key Performance Indicators and a device Quality of Experience, and to determine a root cause of a network problem (such as dropped calls) leading to a diminished Quality of Experience. In some embodiments, the diagnostic files may be aggregated to form a database of aggregated diagnostics, which can be used to further analyze a network to determine the root cause of a network problem. In some embodiments, the aggregated diagnostics may be indexed according to location, time, device type, device problem, or access technology.

Techniques described herein provide quality of experience (QoE) diagnosis and analysis for wireless communications. Implementations include generating, by a user device, a user interaction event. The user device may obtain data associated with one or more QoE metrics, and cause analysis on the data associated with the one or more metrics to identify QoE issues.

Quality of experience diagnosis and analysis in wireless communications

The power consumption of a user device may be monitored as an application on the user device performs one or more actions. Device commands for the user device and monitoring commands for a power monitor may be generated based on a script. The script is created to trigger an application on a user device to perform one or more actions while power consumption of the user device is monitored. The device commands are sent to the application on the user device to trigger the application to perform the one or more actions. The monitoring commands are sent to the power monitor to command the power monitor to track the power consumption of the use device as the application performs one or more actions. Subsequently, power consumption data for the user device are received from the power monitor, and device event data are received from the user device for analysis.

User device power consumption monitoring and analysis

A telecommunication carrier may use data collected from user devices to pinpoint sources of data interruptions in the communication connection between a carrier network and the user devices. The telecommunication carrier may receive multiple reports of detected data interruptions at one or more computing devices of a carrier network. The detected data interruptions include a data interruption to a user device that is detected by an agent application on the user device that monitors of at least one of an audio pathway or a video pathway of the user device. The telecommunication carrier may aggregate data interruption information from the multiple reports of the detected data interruptions, and analyze the data interruption information to pinpoint one or more sources of a detected data interruption.

Growing community of inventors

Renton, WA, United States of America

Kranthi Sontineni