View source for Telefonaktiebolaget lm ericsson (publ) (20240187320). Estimating Quality Metric for Latency Sensitive Traffic Flows in Communication Networks simplified abstract

=Estimating Quality Metric for Latency Sensitive Traffic Flows in Communication Networks=


==Organization Name==

[[:Category:telefonaktiebolaget lm ericsson (publ)|telefonaktiebolaget lm ericsson (publ)]]


[[Category:telefonaktiebolaget lm ericsson (publ)]]

==Inventor(s)==

[[:Category:Göran Eriksson of Norrtälje (SE)|Göran Eriksson of Norrtälje (SE)]][[Category:Göran Eriksson of Norrtälje (SE)]]

[[:Category:Magnus Westerlund of Upplands Väsby (SE)|Magnus Westerlund of Upplands Väsby (SE)]][[Category:Magnus Westerlund of Upplands Väsby (SE)]]

[[:Category:Stefan H�kansson of Göteborg (SE)|Stefan H�kansson of Göteborg (SE)]][[Category:Stefan H�kansson of Göteborg (SE)]]

[[:Category:Gunnar Heikkil� of Gammelstad (SE)|Gunnar Heikkil� of Gammelstad (SE)]][[Category:Gunnar Heikkil� of Gammelstad (SE)]]

==Estimating Quality Metric for Latency Sensitive Traffic Flows in Communication Networks - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240187320 titled 'Estimating Quality Metric for Latency Sensitive Traffic Flows in Communication Networks

==Simplified Explanation==

The patent application describes an apparatus in a mobile communication network that predicts quality of experience (QoE) and quality of service (QoS) based on monitoring IP flows carrying latency-sensitive content and application behavior information.

* The apparatus combines information from monitoring IP flows with data about application behavior and target QoE/QoS to provide ongoing predictions of QoE/QoS.
* It may use a probe on a device to generate traffic for learning flow characteristics not obtained from monitoring application IP flows in the network.
* The technology can predict quality metrics for applications where jitter/latency affects perceived quality, such as QoE for human consumers or QoS for machine-type communications.
* It is applicable to analyzing traffic carrying conversational speech.

== Potential Applications ==
The technology can be applied in various industries such as telecommunications, video streaming services, online gaming, and IoT devices to improve user experience and network performance.

== Problems Solved ==
1. Predicting QoE/QoS for latency-sensitive content.
2. Enhancing network performance by analyzing flow characteristics.

== Benefits ==
1. Improved user satisfaction with better quality of experience.
2. Enhanced network efficiency and performance optimization.

== Potential Commercial Applications ==
Optimizing network performance for telecommunications providers, improving video streaming services, enhancing online gaming experiences, and optimizing IoT device connectivity.

== Possible Prior Art ==
There may be prior art related to network monitoring and QoE/QoS prediction technologies in the field of telecommunications and network optimization.

=== What are the potential security implications of implementing this technology in a mobile communication network? ===

Implementing this technology in a mobile communication network could raise security concerns related to the collection and analysis of sensitive data flowing through the network. Ensuring data privacy and secure data transmission would be crucial to address these implications.

=== How does this technology compare to existing QoE/QoS prediction methods in terms of accuracy and efficiency? ===

Comparing this technology to existing QoE/QoS prediction methods would provide insights into its effectiveness in accurately predicting quality metrics and optimizing network performance. Conducting performance evaluations and benchmarking tests could help determine its advantages over traditional methods.


==Original Abstract Submitted==

an apparatus in a mobile communication network combines information from monitoring ip flows carrying latency sensitive content passing the apparatus and information about the application behavior and target quality of experience (qoe) or target connectivity characteristics such as quality of service (qos) from the application to provide ongoing predictions of qoe/qos. in some cases, the apparatus exploits a probe on a device to generate traffic for learning flow characteristics not obtained from monitoring application ip flows in the network. embodiments disclosed herein can be used to predict quality metrics for many applications where jitter/latency is a factor affecting perceived quality, such as qoe for a human consumer or qos for machine type communications. the embodiments are applicable to the analysis of traffic carrying conversational speech.



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