Intelligent Data Restoration Prioritizing High-Priority Items
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of restoring data to a computing device from cloud storage can consume significant computing resources, leading to suboptimal device performance, increased battery usage, and heat generation, negatively impacting the user experience.
Innovation Solution
A system that prioritizes data restoration based on user usage patterns, deferring low-priority data restoration until favorable device conditions such as energy and data budgets, and thermal conditions allow, minimizing resource consumption and maintaining device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data restoration is performed using conventional methods, then complete data recovery is achieved, but device performance deteriorates due to high resource consumption
Solution Approach 1:
The patent segments the data restoration process into multiple phases: initial quick restore of essential data, and subsequent background restore of remaining data. This segmentation allows the system to restore critical data quickly while deferring non-critical data to periods of lower device utilization, thereby maintaining device performance during restoration operations.
Solution Approach 2:
The patent implements dynamic adjustment of restoration behavior based on real-time device conditions such as battery level, network connectivity, and device usage patterns. The system adapts its restoration strategy by pausing or resuming operations dynamically, ensuring that restoration occurs optimally without consistently degrading device performance.
2Speed
If data restoration is performed continuously, then restoration speed is improved, but energy consumption increases
Solution Approach 1:
The patent employs periodic restoration operations instead of continuous restoration. The system schedules restoration tasks to execute during specific time windows when device usage is low, using periodic checks of device conditions to determine when to proceed with restoration. This approach maintains reasonable restoration speed while significantly reducing overall battery consumption compared to continuous operation.
Solution Approach 2:
The system continuously monitors device conditions including battery level, network status, and device usage, using this feedback to dynamically adjust restoration behavior. When feedback indicates low battery or high device usage, the system pauses or defers restoration operations, thereby optimizing the balance between restoration speed and energy consumption.
3Ease of operation
If high priority data is restored first, then user accessibility is improved, but total restoration time increases
Solution Approach 1:
The patent segments the data set into priority tiers (high, medium, low) and restores them in sequence. High-priority data that users need immediately is restored first during initial setup, while lower-priority data is restored subsequently in the background. This segmentation improves user accessibility early on while managing total restoration time through efficient background processing of remaining data.
Solution Approach 2:
The system performs preliminary identification and prioritization of data during the restoration planning phase, determining which data should be restored first based on user patterns and device characteristics. This preliminary action enables the system to prepare an optimized restoration sequence that improves early user accessibility while laying the groundwork for efficient completion of the full restoration process.
Data Source
AI summary
In some implementations, a system can intelligently restore data to a user's computing device. For example, the system can prioritize data to be restored to a user device based on the data that the user is most likely to use. The system can restore high priority data items first while delaying restoration of low priority data items. The system can control when data restoration is performed based on device conditions. For example, the device conditions can include how much of an energy budget and/or data budget remains for downloading data to the user device. The device conditions can include the thermal condition (e.g., how hot) of the user device. If device conditions do not allow for downloading data at a particular time, then the device can delay downloading data until the device conditions allow for downloading and/or restoring the data.