Application Update Prioritization Using ML Usage Scoring
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Solution Overview
Problem
Managing application updates on computing devices is challenging due to the number of updates needed, leading to inefficient prioritization that can result in unsuccessful installations of important applications and unnecessary downloads of unimportant applications, which increases bandwidth, processor, and power usage.
Innovation Solution
A computing device and/or cloud system applies a machine learning model to application usage history, update installation history, and network settings to determine update priority scores, prioritizing updates for frequently used and likely successful installations while reducing unnecessary downloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic updates are applied to all applications equally, then installation automation is improved, but resource efficiency (bandwidth, processor, power) deteriorates due to unnecessary downloads and failed installations
Solution Approach 1:
The system changes the parameter of update priority by applying a machine learning model that assigns different priority scores to different applications based on usage history and installation success probability, transforming the uniform update approach into a differentiated prioritization system
Solution Approach 2:
The patent replaces the mechanical/random update selection process with an intelligent machine learning-based prioritization system that analyzes multiple factors (usage history, installation success rate, user preferences) to determine update priorities, substituting automated decision-making with AI rather than simple automation
2Reliability
If updates are prioritized based on machine learning analysis, then update success rate and resource efficiency are improved, but system complexity increases due to the machine learning model
Solution Approach 1:
The machine learning model acts as an intermediary component between the update management system and the applications, analyzing usage data and installation histories to generate priority scores without requiring complex changes to the core update installation mechanism
Solution Approach 2:
The system implements feedback loops where installation results and usage patterns are continuously fed back into the machine learning model to refine priority predictions, improving reliability over time while managing complexity through iterative learning rather than complex rule-based systems
3Loss of time
If all application updates are downloaded and installed, then application freshness is improved, but bandwidth and storage resources are wasted on unimportant applications
Solution Approach 1:
The system applies local quality by treating different applications differently based on their individual characteristics and user importance, assigning high priority to frequently used applications and low or zero priority to rarely used applications, rather than applying uniform update treatment to all applications
Solution Approach 2:
The system performs partial action by selectively updating only the most important applications rather than all applications, accepting that some less important applications may remain at older versions to conserve bandwidth and storage resources
Data Source
AI summary
A computing system may receive a request for application update information for one or more applications installed on a computing device. The computing system determines, based on the request for the application update information, a set of applications that need to be updated and applies a machine learning model to determine a respective update priority score for each application from the set of applications. The computing system sends an indication of the respective update priority scores for the set of applications to the computing device. Responsive to receiving an update request from the computing device, the computing system initiates installation of the pending update for the at least one application from the set of applications.