Autosave Trigger Configuration Using Machine Learning Value Metrics
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Solution Overview
Problem
Existing autosave mechanisms in software applications are not customizable enough to effectively capture vital data without interrupting the application or capturing too much or too little data, leading to inefficiencies and potential data loss.
Innovation Solution
A machine learning model is used to generate a value metric for autosave triggers based on the cost and benefit of the autosave operation, allowing the system to determine whether to apply the trigger by analyzing data vulnerability and environmental conditions, thereby configuring autosave triggers dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a regularly-occurring autosave is implemented, then data loss is reduced, but application performance is degraded due to interruptions
Solution Approach 1:
The patent implements dynamic autosave triggering based on real-time system state detection. Instead of fixed interval autosaves, the system monitors conditions such as data changes, user actions, and system events to dynamically determine when autosave is needed. This allows the system to adapt the autosave behavior to actual usage patterns, performing autosave only when necessary to prevent data loss while avoiding unnecessary interruptions during normal operation.
Solution Approach 2:
The system changes the parameter of autosave frequency from a static fixed interval to a dynamic condition-based trigger. By monitoring system parameters such as data modification events, user interaction patterns, and application state changes, the system adjusts the autosave timing accordingly. This parameter change enables the system to maintain reliability by saving data when changes occur while improving productivity by avoiding unnecessary autosave operations.
2Reliability
If a regularly-occurring autosave is implemented, then data is captured periodically, but too much or too little data is captured
Solution Approach 1:
The system implements feedback mechanisms that monitor data changes and system state in real-time. This feedback allows the autosave system to detect when actual data modifications occur and trigger autosave accordingly. The feedback loop ensures that data is captured when changes happen (completeness) while avoiding unnecessary captures during stable states (data volume control).
Solution Approach 2:
The system performs preliminary detection of data change conditions before executing the autosave operation. By monitoring for specific triggers such as data modification events, user actions, or system state changes in advance, the system can determine whether an autosave operation is actually needed. This preliminary action prevents both under-capture (by detecting changes early) and over-capture (by avoiding unnecessary saves).
3Measurement precision
If a machine learning model is used to determine autosave triggers, then data retention precision is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated machine learning models that automatically learn optimal autosave triggering patterns from historical data and user behavior. The ML model continuously improves its predictions by analyzing past autosave effectiveness and system state, reducing the need for manual configuration and complex rule-based systems. This self-service approach improves precision while keeping the added complexity manageable through automation rather than manual intervention.
Solution Approach 2:
The patent replaces complex mechanical (rule-based) autosave decision systems with a machine learning-based intelligent system. Instead of using elaborate if-then rules and multiple conditional checks, the system uses ML models to predict optimal autosave timing based on learned patterns from historical data. This substitution reduces the complexity of the decision-making logic while improving precision through data-driven predictions.
Data Source
AI summary
Techniques for configuring autosave triggers in a computing environment based on environment and data conditions are disclosed. A system trains a machine learning model based on data attributes and environmental attributes to generate autosave value triggers for a computing environment. The autosave value triggers are triggered by different conditions. For example, one autosave trigger may be triggered when an error condition is detected. Another may be triggered when a certain number of operations are performed. The machine learning model generates autosave trigger values scores for one or more autosave triggers. The system may implement the autosave triggers in the computing environment based on the autosave trigger values.