Display Interface for Subscriber Device Configuration Diagnostics

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Solution Overview

Problem

RF drive tests for assessing mobile network coverage and quality of service are resource-intensive and time-consuming, and users face challenges in configuring their devices for optimal network performance without operator access privileges.

Innovation Solution

A system that collects and validates wireless network signals from subscriber devices, using machine learning to diagnose malfunctions by comparing device configuration parameters and network usage information, allowing configuration adjustments without operator access, and providing alerts or reconfigurations to improve performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If RF drive tests are used to assess mobile network coverage and quality of service, then measurement precision is improved, but loss of time and productivity deteriorate due to resource-intensive and time-consuming nature

Engineering Contradiction:

Improvenetwork signal measurement precisionVSAvoiddrive test time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent uses subscriber devices (mobile phones, tablets) as copies of professional RF drive test equipment. These devices collect network signal data through applications that mimic drive test functionality, eliminating the need for dedicated drive test vehicles and personnel while maintaining measurement capabilities through machine learning analysis of the collected data

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system enables subscriber devices to self-configure network parameters and self-diagnose connectivity issues without requiring operator intervention. The machine learning model automatically analyzes collected data, identifies malfunctions, and provides configuration recommendations, allowing devices to serve their own diagnostic and optimization needs

Inventive Principle:

Principle #25Self-service

2Device complexity

If operator access privileges are required to configure device parameters for optimal network performance, then device complexity is reduced, but ease of operation deteriorates for users without operator access

Engineering Contradiction:

Improveconfiguration management simplicityVSAvoiduser configuration ease

Core Design Contradiction:

Device complexityVSEase of operation

Solution Approach 1:

The patent introduces a machine learning model as an intermediary between users and device configuration parameters. The model translates user-friendly diagnostic information into specific configuration recommendations, bridging the gap between simple user operations and complex technical parameters that would otherwise require operator privileges to modify

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements continuous feedback loops where collected network data is analyzed by machine learning models, which then provide configuration recommendations back to users. This feedback mechanism enables users to iteratively optimize their device settings based on actual network performance without needing to understand complex configuration parameters or obtain operator access

Inventive Principle:

Principle #23Feedback

3Measurement precision

If comprehensive device configuration parameters are collected for diagnostics, then measurement precision is improved, but loss of information increases due to privacy and security concerns

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidconfiguration parameter privacy

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent applies local quality by processing and analyzing configuration parameters locally on the subscriber device using machine learning models. Only anonymized diagnostic results and aggregated statistics are transmitted to the network operator, while sensitive raw configuration data remains on the user's device, thereby maintaining diagnostic accuracy while protecting user privacy

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system transforms sensitive configuration parameters into anonymized diagnostic indicators through parameter changes. The machine learning model processes raw configuration data locally and outputs transformed, privacy-preserving metrics that maintain diagnostic value while removing personally identifiable information and sensitive device-specific details

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250280313A1Display interface for device configurations, and network service diagnostics

Publication Date: 2025.09.04 OCTOLYTICS INC

AI summary

In one embodiment, a display system includes a display interface and a memory storing instructions. When the instructions are executed by the one or more processors, the system is configured to display in the display interface: a number of terminals with associated terminal device information, APN information, VOLTE information, and current device configuration parameters. For each terminal, the system displays a number of configuration logs of the terminal, each configuration log indicating a time of change of configuration settings of the terminal, a previous configuration setting, and a current configuration setting of the terminal. The system displays a number of historical snapshots of diagnostics information reported by the terminal, each of the historical snapshots including customer experience information reported by the terminal, a location information reported by the terminal, and a time of capture of the historical snapshot.