Self-optimizing Cache for Medical Imaging Workflows
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Solution Overview
Problem
Existing pre-fetching strategies in medical imaging are static and do not adapt to dynamic user behavior or individual hospital environments, leading to inefficient image data access and loading times.
Innovation Solution
A self-optimizing caching strategy using a proxy service that collects metadata to dynamically adjust pre-fetching based on user behavior and environment, reducing image loading times and optimizing workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static pre-fetching rules are used, then implementation simplicity is maintained, but adaptability to user behavior and environment deteriorates
Solution Approach 1:
The patent transforms the static pre-fetching system into a dynamic one by introducing machine learning models that continuously learn from user access patterns and automatically adjust pre-fetching strategies. The system adapts to changing user behavior and environmental conditions in real-time, resolving the contradiction between implementation simplicity and adaptability.
Solution Approach 2:
The system employs self-learning algorithms that automatically optimize pre-fetching parameters without requiring manual configuration or intervention. The machine learning models autonomously analyze metadata, identify patterns, and adjust pre-fetching strategies, enabling the system to serve itself and adapt to user needs dynamically.
2Reliability
If brute-force pre-fetching of all studies is performed, then availability of data is improved, but network traffic and storage requirements worsen
Solution Approach 1:
Instead of prefetching all studies indiscriminately, the system applies partial action by using machine learning models to predict and prefetch only the specific studies that users are likely to access. This selective approach maintains data availability for needed studies while significantly reducing unnecessary network traffic and storage consumption.
Solution Approach 2:
The system dynamically changes pre-fetching parameters such as prefetch quantity, timing, and target studies based on learned user patterns and current system state. This adaptive parameter adjustment optimizes the balance between ensuring data availability and minimizing network traffic and storage usage.
3Device complexity
If static table-driven pre-fetching is used, then system complexity is reduced, but optimization potential deteriorates
Solution Approach 1:
The patent introduces feedback loops where the system continuously monitors user access patterns, evaluates the effectiveness of pre-fetching operations, and uses this feedback to refine its predictions and strategies. Machine learning models process this feedback to automatically optimize pre-fetching performance, resolving the contradiction between system complexity and optimization potential.
Solution Approach 2:
The system replaces static table-driven mechanical pre-fetching rules with intelligent machine learning-based decision-making. This substitution enables the system to achieve high optimization potential through adaptive algorithms while managing complexity through automated learning rather than manual rule configuration.
Data Source
AI summary
A system and appertaining method provide for pre-fetching records from a central data base to a local storage area in order to reduce delays associated with the data transfers. User patterns for requesting data records are analyzed and rules/strategies are generated that permit an optimal pre-fetching of the records based on the user patterns. The rules/strategies are implemented by a routine that pre-fetches the data records so that users have the records available to them when needed.