Automated Software Update Scheduling Around Predicted Data Usage

Solution Overview

Problem

Conventional software update processes on mobile devices require user intervention, leading to potential security risks and disruptions when updates are needed, especially for critical updates.

Innovation Solution

An automated system determines an optimal time for software updates based on historical data usage patterns and criticality, minimizing disruptions by scheduling the installation at a time that aligns with minimal data usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If automated software update installation is implemented, then security risks are reduced and user intervention is eliminated, but disruptions to data usage may increase

Engineering Contradiction:

ImprovesecurityVSAvoiddisruption to data usage

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary analysis of data usage patterns before scheduling updates. By predicting future data usage schedules based on historical patterns, the system proactively identifies optimal time windows for updates that minimize disruption, rather than simply forcing updates at fixed intervals.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The update scheduling system dynamically adapts to changing user behavior patterns. It continuously monitors actual data usage against predicted schedules and adjusts future update windows accordingly, making the system flexible rather than rigid in its update timing.

Inventive Principle:

Principle #15Dynamics

2Stability of the object's composition

If software updates are installed at fixed intervals, then update consistency is maintained, but user convenience deteriorates due to unexpected disruptions

Engineering Contradiction:

Improveupdate consistencyVSAvoiduser convenience

Core Design Contradiction:

Stability of the object's compositionVSEase of operation

Solution Approach 1:

The system performs preliminary analysis of data usage patterns before scheduling updates. By predicting future data usage schedules based on historical patterns, the system proactively identifies optimal time windows for updates that minimize disruption, rather than simply forcing updates at fixed intervals.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback loops where actual data usage measurements are compared against predicted schedules. This feedback mechanism allows the system to learn from discrepancies and continuously improve its prediction accuracy, ensuring updates are scheduled at times that truly minimize user disruption.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If manual user approval is required for each update, then user control is maximized, but update timeliness and security response deteriorate

Engineering Contradiction:

Improveuser controlVSAvoidupdate timeliness

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system performs self-service by automatically analyzing data usage patterns, predicting optimal update windows, and scheduling updates without requiring continuous user intervention. The mobile device itself generates and processes the scheduling decisions based on its own usage history.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of data usage patterns before scheduling updates. By predicting future data usage schedules based on historical patterns, the system proactively identifies optimal time windows for updates that minimize disruption, rather than simply forcing updates at fixed intervals.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260056726A1Techniques for optimized automated software update implementation

Publication Date: 2026.02.26 T MOBILE US INC

AI summary

Techniques are described herein for implementing automated installation of software updates on user equipment while minimizing disruptions to predicted data usage. In embodiments, such techniques may comprise upon identifying a software update to be implemented on a user equipment in communication with the network node, determining, based on information about the software update, a time window within which the software update is to be implemented. The techniques may further comprise generating, based on information about the user equipment, a predicted data usage schedule associated with the time window, determining, based on the predicted data usage schedule, a start time for an installation period associated with the software update, and providing instructions to the user equipment to cause the user equipment to install the software update at the start time.