Automatic Scheduling of Recognition Tasks via Dependency Networks
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Solution Overview
Problem
Conventional medical imaging techniques face challenges in efficiently scheduling recognition tasks for anatomical structure identification due to varying task durations, processor availability, and complexity, leading to manual scheduling difficulties and inefficient software development.
Innovation Solution
A novel environment is created that automatically schedules recognition tasks using dependency networks and project planning methods, allowing dynamic assignment to multiple processors and phases, enabling efficient execution and flexible integration of algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling of recognition tasks is performed, then scheduling control is possible, but time consumption increases extremely and efficiency decreases
Solution Approach 1:
The system enables automatic self-scheduling of recognition tasks through a scheduling module that autonomously determines task execution order and processor allocation based on dependency networks and available resources, eliminating the need for manual scheduling intervention and significantly reducing time consumption
Solution Approach 2:
The patent replaces manual mechanical scheduling operations with an automated computational scheduling system that uses project planning methods and dependency analysis to dynamically generate and adjust task schedules, transforming manual control into automated intelligent management
2Adaptability or versatility
If the number of recognition tasks increases, then recognition completeness improves, but system complexity increases quickly
Solution Approach 1:
The patent segments the complex recognition system into modular tasks with defined inputs and outputs, organizing them into dependency networks that can be independently managed and scheduled, allowing the system to handle increasing numbers of tasks without proportionally increasing overall complexity
Solution Approach 2:
The scheduling module serves multiple functions simultaneously: it manages task dependencies, allocates processors, handles dynamic resource availability, and optimizes execution schedules, providing a universal control mechanism that manages complexity across varying numbers of recognition tasks
3Productivity
If dynamic task scheduling is implemented, then processor utilization improves, but scheduling complexity increases
Solution Approach 1:
The system implements dynamic scheduling where the scheduling module continuously monitors processor availability and task progress, automatically adjusting task allocation and execution timing in real-time to optimize processor utilization while managing scheduling complexity through adaptive rather than static rules
Solution Approach 2:
The scheduling system incorporates feedback mechanisms that monitor task execution status and processor availability, using this information to dynamically adjust scheduling decisions and improve processor utilization while maintaining manageable complexity through data-driven adaptation
Data Source
AI summary
Embodiments of the present invention are directed to techniques for providing an environment for the efficient execution of recognition tasks. A novel environment is provided which automatically and efficiently executes a recognition program on as many computer processors as available. This program, deconstructed into separate tasks, may be executed by constructing a dependency network from known inputs and outputs of the tasks, applying project planning methods for scheduling these tasks into multiple processing threads, and dynamically assigning tasks within these threads to processors. Therefore, an efficient schedule of tasks to complete a recognition program can be created and executed automatically, for any type of recognition problem. The system will not only allow for the ability to leverage multiple processors for efficiently generating variable and customizable automatically created schedules, but will also still maintain the flexibility to use serial programming in recognition algorithms for individual objects, properties, or features.