Application State Prediction for Seamless Supplemental Content Timing

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Solution Overview

Problem

Existing systems throttle user experience by artificially delaying content presentation or inserting supplemental content, leading to frustration and reduced user engagement, particularly in cloud-based applications where resource providers and content creators face conflicts due to these practices.

Innovation Solution

Predicting application state transitions using machine learning models based on hardware and application heuristics to identify periods where supplemental content can be provided without affecting user interaction, allowing seamless integration of supplemental content during natural loading times.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If supplemental content is inserted during content presentation, then resource provider receives incentivization, but user experience is degraded

Engineering Contradiction:

Improveresource provider incentivizationVSAvoiduser experience

Core Design Contradiction:

Loss of energyVSEase of operation

Solution Approach 1:

The system performs preliminary actions by predicting application state transitions before they occur using machine learning models. This allows supplemental content to be prepared and inserted at optimal moments (during natural loading times) before the user would otherwise experience delays, thus providing incentivization to resource providers without degrading user experience.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts content insertion based on predicted application states. By continuously monitoring hardware metrics and application performance, the system adapts its content insertion strategy in real-time, inserting supplemental content only during periods when the application is naturally loading or transitioning, thereby maintaining smooth user experience while achieving resource provider incentivization.

Inventive Principle:

Principle #15Dynamics

2Loss of energy

If artificial throttling is applied to ensure resource provider incentivization, then service sustainability is improved, but content presentation quality is degraded

Engineering Contradiction:

Improveservice sustainabilityVSAvoidcontent presentation quality

Core Design Contradiction:

Loss of energyVSManufacturing precision

Solution Approach 1:

The system predicts application state transitions in advance using machine learning models trained on hardware metrics and application performance data. This preliminary prediction allows the system to insert supplemental content during natural loading periods before the user would otherwise experience artificial delays, ensuring service sustainability through resource provider incentivization while maintaining content presentation quality.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system converts the naturally occurring loading times and state transitions (which would otherwise be perceived as delays or inefficiencies) into beneficial opportunities for supplemental content insertion. By identifying and utilizing these natural transition periods, the system achieves resource provider incentivization without creating artificial throttling that would degrade content presentation quality.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

3Loss of energy

If supplemental content is inserted during important content points, then resource provider receives compensation, but content provider satisfaction is reduced

Engineering Contradiction:

Improveresource provider compensationVSAvoidcontent provider satisfaction

Core Design Contradiction:

Loss of energyVSReliability

Solution Approach 1:

The system uses machine learning models to predict application state transitions before they occur, identifying natural loading periods and transition states. This preliminary action ensures that supplemental content is inserted only during these predicted transition periods, never during important content points, thereby achieving resource provider compensation while maintaining content provider satisfaction.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback from application state monitoring and user interaction data to continuously refine its content insertion strategy. By analyzing feedback from content providers about important scenes and moments, the system adjusts its prediction model to avoid inserting supplemental content during critical content points, ensuring both resource provider compensation and content provider satisfaction.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12610094B2Predicting application states for supplemental content insertion

Publication Date: 2026.04.21 NVIDIA CORP

AI summary

Approaches presented herein provide systems and methods for determining different states for distributed computing processes based on information acquired from underlying hardware. Different tasks may be executed by hardware for a given distributed computing process having a certain hardware configuration and for a given application. Telemetry information may be acquired to identify different states according to the telemetry information independent from underlying application engines. Thereafter, identification of different application states enables prediction of time periods between various application states, which may provide opportunities for additional processing tasks, such as providing supplemental content.