Predictive Link Service Profiles for Vehicle App Connectivity

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Solution Overview

Problem

Applications on vehicles are unaware of changing link conditions and attempt to execute functions that are not supported during varying internet connectivity, leading to inefficiencies and user confusion.

Innovation Solution

Generate predictive link service profiles based on historical and real-time link characteristics to inform applications of expected connectivity, allowing them to modify their behavior accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If service control decisions are based on real-time network conditions only, then responsiveness to current network state is improved, but service quality degrades when network conditions change between measurement and execution

Engineering Contradiction:
Improveresponsiveness to network stateVSAvoidservice quality consistency
Core Design Contradiction:
SpeedVSReliability

Solution Approach 1:

The system performs preliminary actions by measuring network conditions ahead of time and storing them in a cache. The service control decision is made based on these pre-measured conditions, and the decision is held until execution time. This preliminary measurement and caching approach ensures that the service quality is determined based on stable, pre-assessed network conditions rather than potentially fluctuating real-time conditions, thus maintaining service quality consistency while still being responsive to network state.

Inventive Principle:
Principle #10Preliminary action

2Measurement precision

If network conditions are measured frequently to maintain accuracy, then measurement precision is improved, but system resource consumption increases

Engineering Contradiction:
Improvenetwork condition accuracyVSAvoidsystem resource consumption
Core Design Contradiction:
Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system implements periodic action by measuring network conditions at scheduled intervals rather than continuously. The measurement frequency is optimized to balance accuracy requirements with resource consumption. Between measurements, the system uses cached network condition data for service control decisions. This periodic measurement approach maintains sufficient measurement precision for accurate service control while significantly reducing system resource consumption compared to continuous monitoring.

Inventive Principle:
Principle #19Periodic action

3Productivity

If service control decisions are cached for later execution, then service responsiveness is improved, but decision accuracy may degrade due to network condition changes

Engineering Contradiction:
Improveservice execution efficiencyVSAvoiddecision accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary service control decision-making based on pre-measured network conditions and caches both the decision and the associated network condition data. When execution time arrives, the system retrieves the cached decision and validates it against current network conditions. This preliminary decision-making approach improves service execution efficiency by avoiding real-time computation delays while maintaining decision accuracy through validation and refresh mechanisms that update cached decisions when network conditions change significantly.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentEP4140121B1Link service profile for predictive service control
Publication Date: 2026.04.29 GOGO BUSINESS AVIATION LLC
  • EP4140121B1 patent drawingFigure 1A
  • EP4140121B1 patent drawingFigure 1B
  • EP4140121B1 patent drawingFigure 2

AI summary

Systems and methods for modifying application performance in view of link conditions are provided. An example method includes predicting respective link rates of at least one link of a plurality of links that deliver data to and from a vehicle while the vehicle is moving from an origination point to a destination point. The predicting is based on historical link characteristics. The method further includes generating a link service profile of the vehicle based on the predicted respective link rates and a route of the vehicle. In addition, the method includes receiving a request for information related to the link service profile from a mobile device and transmitting, to the mobile device, an indication based on the link service profile, thereby causing a modification of a behavior of an application executing at the mobile device based on the link service profile.