Configuring communications network parameters

Patent metadata

Field	Value
Publication number	US-2025392524-A1
Application number	US-202519244596-A
Country	US
Kind code	A1
Filing date	Jun 20, 2025
Priority date	Jun 21, 2024
Publication date	Dec 25, 2025
Grant date	—

Abstract

As disclosed herein, a computer-implemented method for configuring communications network parameters using quality of experience metrics is provided. The computer-implemented method may include determining, by a computing platform, a quality of experience metric of an application associated with the computing platform. The computer-implemented method may include providing the quality of experience metric to an artificial intelligence model executing on the computing platform. The computer-implemented method may include determining, by the artificial intelligence model and based on the quality of experience metric, a recommended configuration for a communications network associated with the computing platform. The computer-implemented method may include providing the recommended configuration to an operator of the communications network. A system and a non-transitory computer-readable storage medium are also disclosed.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
determining, by a first computing platform, a first quality of experience (QoE) metric of a first application associated with the first computing platform; providing the first QoE metric to an artificial intelligence (AI) model executing on the first computing platform; determining, by the AI model and based on the first QoE metric, a recommended configuration for a communications network associated with the first computing platform; and providing the recommended configuration to an operator of the communications network.

2 . The computer-implemented method of claim 1 , wherein the first QoE metric includes at least one of an interaction metric, a perception metric, and an outcome metric.

3 . The computer-implemented method of claim 1 , further comprising:
receiving, from a second computing platform, a second QoE metric of a second application associated with the second computing platform; assigning a first QoE weight to the first QoE metric and a second QoE weight to the second QoE metric; and generating a composite QoE metric based on the first QoE weight, the first QoE metric, the second QoE weight, and the second QoE metric.

4 . The computer-implemented method of claim 3 , wherein providing the first QoE metric to the AI model includes providing the composite QoE metric to the AI model.

5 . The computer-implemented method of claim 3 , further comprising:
determining a platform infrastructure metric associated with the first computing platform; determining a radio network metric associated with a client device hosting the first application; assigning a first user experience (UX) weight to the composite QoE metric, a second UX weight to the platform infrastructure metric, and a third UX weight to the radio network metric; and generating a composite UX score based on the first UX weight, the composite QoE metric, the second UX weight, the platform infrastructure metric, the third UX weight, and the radio network metric.

6 . The computer-implemented method of claim 5 , wherein the platform infrastructure metric includes at least one of an availability metric, a bandwidth metric, a bitrate metric, a throughput metric, a jitter metric, a packet loss metric, and a latency metric.

7 . The computer-implemented method of claim 5 , wherein providing the first QoE metric to the AI model includes providing the composite UX score to the AI model.

8 . The computer-implemented method of claim 1 , wherein the AI model is trained based on UX data and network configuration data.

9 . The computer-implemented method of claim 8 , wherein the UX data is associated with at least one computing platform, and wherein the network configuration data is associated with at least one communications network supporting the at least one computing platform.

10 . The computer-implemented method of claim 8 , wherein the AI model is trained by mapping the UX data to the network configuration data.

11 . A system, comprising:
one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations including:
determining, by a first computing platform, a first quality of experience (QoE) metric of a first application associated with the first computing platform;
providing the first QoE metric to an artificial intelligence (AI) model executing on the first computing platform;
determining, by the AI model and based on the first QoE metric, a recommended configuration for a communications network associated with the first computing platform; and
providing the recommended configuration to an operator of the communications network.

12 . The system of claim 11 , wherein the first QoE metric includes at least one of an interaction metric, a perception metric, and an outcome metric.

13 . The system of claim 11 , wherein the operations further include:
receiving, from a second computing platform, a second QoE metric of a second application associated with the second computing platform; assigning a first QoE weight to the first QoE metric and a second QoE weight to the second QoE metric; and generating a composite QoE metric based on the first QoE weight, the first QoE metric, the second QoE weight, and the second QoE metric.

14 . The system of claim 13 , wherein providing the first QoE metric to the AI model includes providing the composite QoE metric to the AI model.

15 . The system of claim 13 , wherein the operations further include:
determining a platform infrastructure metric associated with the first computing platform; determining a radio network metric associated with a client device hosting the first application; assigning a first user experience (UX) weight to the composite QoE metric, a second UX weight to the platform infrastructure metric, and a third UX weight to the radio network metric; and generating a composite UX score based on the first UX weight, the composite QoE metric, the second UX weight, the platform infrastructure metric, the third UX weight, and the radio network metric.

16 . The system of claim 15 , wherein the platform infrastructure metric includes at least one of an availability metric, a bandwidth metric, a bitrate metric, a throughput metric, a jitter metric, a packet loss metric, and a latency metric.

17 . The system of claim 15 , wherein providing the first QoE metric to the AI model includes providing the composite UX score to the AI model.

18 . The system of claim 11 , wherein the AI model is trained based on UX data and network configuration data.

19 . The system of claim 18 , wherein:
the UX data is associated with at least one computing platform; the network configuration data is associated with at least one communications network supporting the at least one computing platform; and the AI model is trained by mapping the UX data to the network configuration data.

20 . A non-transitory computer-readable storage medium storing instructions encoded thereon that, when executed by a processor, cause the processor to perform operations comprising:
determining, by a first computing platform, a first quality of experience (QoE) metric of a first application associated with the first computing platform; receiving, from a second computing platform, a second QoE metric of a second application associated with the second computing platform; assigning a first QoE weight to the first QoE metric and a second QoE weight to the second QoE metric; generating a composite QoE metric based on the first QoE weight, the first QoE metric, the second QoE weight, and the second QoE metric; providing the composite QoE metric to an artificial intelligence (AI) model executing on the first computing platform; determining, by the AI model and based on the composite QoE metric, a recommended configuration for a communications network associated with the first computing platform; and providing the recommended configuration to an operator of the communications network.

Assignees

• Meta Platforms Inc

Inventors

• Ranganathan Murali

Classifications

• H04L41/5009

  Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] · CPC title

• H04L41/5067

  Customer-centric QoS measurements · CPC title

• H04L43/08

  Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters · CPC title

• H04L41/16 Primary

  using machine learning or artificial intelligence · CPC title

• H04L43/0817 Primary

  by checking functioning · CPC title