Crowdsourcing Task Design Optimization via Model-Based Parameter Inversion
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Solution Overview
Problem
Current crowdsourcing task design methods do not effectively optimize task performance parameters such as accuracy, completion time, and acceptance rate, as they rely on manual adjustments and lack a systematic approach to balance competing objectives.
Innovation Solution
A system and method that utilize a graphical user interface (GUI) and a model to determine optimal input parameters for crowdsourcing tasks based on target output parameters, employing multi-objective optimization techniques like Pareto optimization, and presenting results in graphical plots for requester feedback and iteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustments are used for task design, then ease of operation is maintained, but task performance optimization is insufficient
Solution Approach 1:
The patent replaces manual mechanical adjustment of task parameters with an automated computational system. The system uses a model that takes target output parameters (accuracy, completion time, acceptance rate) as input and automatically computes optimal input parameter values, eliminating the need for manual trial-and-adjustment while maintaining ease of use through a simple interface.
Solution Approach 2:
The task design system performs self-optimization by automatically calculating optimal parameter configurations based on desired performance targets. The system serves itself by internally computing the relationship between input and output parameters through the model, without requiring external expert intervention or complex manual tuning processes.
2Reliability
If multiple output parameters are optimized simultaneously, then task performance is improved, but device complexity increases
Solution Approach 1:
The patent transforms the complexity of multi-parameter optimization into a manageable process by changing the approach from adjusting multiple input parameters simultaneously to specifying desired output parameter targets. The system then computes the corresponding input parameters automatically, reducing perceived complexity while achieving comprehensive optimization of accuracy, completion time, and acceptance rate.
3Reliability
If iterative refinement is implemented, then task performance is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary computation of optimal parameter values before actual task execution. By using the model to pre-calculate the optimal input parameter configuration based on desired output targets, the system eliminates the need for time-consuming iterative adjustments during implementation, achieving both high performance and efficiency.
Solution Approach 2:
The system implements a feedback mechanism where the model continuously refines parameter recommendations based on target performance metrics. This automated feedback loop allows for rapid iteration and optimization without manual intervention, significantly reducing the time required compared to traditional iterative design approaches.
Data Source
AI summary
According to embodiments illustrated herein, a method is provided for designing an image-analysis task. The method includes receiving one or more first target values of one or more output parameters of the image-analysis task from a requester through a GUI. Thereafter, one or more first values of one or more input parameters, associated with the image-analysis task, corresponding to the one or more first target values are determined based on the one of more first target values and a model. The model corresponds to a relationship between the one or more input parameters and the one or more output parameters. Further, the one or more first values of the one or more input parameters are presented to the requester through the GUI. The requester at least provides one or more second target values of the one or more output parameters through the GUI, based on the presentation.