AI-Personalized Network Upgrade Scheduling by User Influence
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Solution Overview
Problem
Existing network upgrades and rollouts cause significant disruptions and negative impacts on users due to inadequate consideration of individual user influences, leading to issues like downtime and increased costs, despite efforts to minimize disruptions through blanket prioritization schemes.
Innovation Solution
A dynamic, hyper-personalized scheduling approach that assesses each user's indirect and direct network influence using machine learning to determine personalized priorities, minimizing negative impacts by adaptively scheduling updates based on user importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If blanket prioritization schemes are used to minimize disruptions, then network upgrade implementation is simplified, but user experience degradation occurs due to inadequate consideration of individual user influences
Solution Approach 1:
The patent segments the network user base into different priority groups based on their influence metrics (direct influence through communications and indirect influence through network position). This segmentation allows differentiated upgrade scheduling where high-influence users receive priority maintenance during low-activity periods, while lower-influence users undergo upgrades during regular maintenance windows, thus resolving the contradiction between implementation simplicity and user experience quality.
Solution Approach 2:
The system dynamically changes the scheduling parameter (upgrade timing) based on user-specific parameters (influence metrics, activity patterns). By calculating direct and indirect influence scores for each user and adjusting upgrade schedules accordingly, the system maintains simple overall implementation while achieving personalized optimization for critical users, thereby improving user experience without significantly complicating the upgrade process.
2Ease of manufacture
If network upgrades are performed during regular maintenance windows, then implementation is straightforward, but significant disruptions and downtime occur for users
Solution Approach 1:
The system performs preliminary assessment of user influence metrics and activity patterns before scheduling upgrades. For high-influence users, the system proactively schedules upgrades during their low-activity periods rather than during standard maintenance windows. This preliminary customization allows straightforward implementation for most users while protecting critical users from disruptions, thus maintaining ease of implementation while improving service continuity.
3Device complexity
If uniform upgrade scheduling is applied to all users, then system management is simplified, but negative impacts increase for influential users
Solution Approach 1:
The patent applies local quality by assigning different upgrade schedules to different user segments based on their specific influence characteristics. High-influence users receive customized scheduling with priority timing during their low-activity periods, while standard users follow uniform maintenance windows. This localized differentiation minimizes negative impacts on influential users while keeping the overall scheduling system manageable through automated influence calculation and segment assignment.
Data Source
AI summary
The present teaching relates to personalized network update. Information on users' network activities is collected and analyzed to identify indirect and direct relations between each user and others. Each user's network influence is determined and represented based on indirect relation embeddings and direct relation embeddings, obtained to characterize the respective indirect and direct relations. A personalized priority for each user is predicted based on the user's representation. A network update schedule is determined based on users' personalized priorities so that network update is conducted in a personalized manner.