Dynamic Task Allocation for Crowdsourcing Platforms
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Solution Overview
Problem
Current systems for managing crowdsourced tasks lack flexibility to adapt to dynamic changes in crowdsourcing platforms, such as new entries, platform improvements, and pricing changes, which can affect the best service offering over time.
Innovation Solution
A computer-implemented method and system that distributes tasks to multiple crowdsourcing platforms based on predefined conditions, updates these conditions using verification data received at intervals, and redistributes tasks accordingly, utilizing a distribution module, verification module, and reward module to optimize task allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If task allocation is based on static benchmarked data, then the system is simple to operate, but it cannot adapt to dynamic changes in crowdsourcing platforms
Solution Approach 1:
The patent implements dynamic task allocation by continuously updating the distribution weights of crowdsourcing platforms based on real-time performance feedback. The system transitions from static benchmarked data to dynamic weight adjustment, where the weight of each platform is modified according to its recent task completion quality and speed, enabling adaptation to changing platform capabilities
Solution Approach 2:
The system establishes a feedback loop where task completion results are collected and used to update the distribution weights. The feedback mechanism compares actual task outcomes against expected outcomes, and this information is fed back into the allocation algorithm to adjust future task distribution, creating a self-improving system
2Productivity
If task allocation is manually optimized, then the allocation can be precise, but it is time-consuming and tedious
Solution Approach 1:
The system implements self-service by automatically performing task allocation without manual intervention. The algorithm autonomously distributes tasks to appropriate crowdsourcing platforms based on current performance data, eliminating the need for manual platform selection and optimization while maintaining high allocation efficiency
Solution Approach 2:
The patent replaces manual mechanical optimization with an automated computational system. The distribution algorithm uses mathematical models to optimize task allocation, substituting human judgment and manual processes with automated calculations that rapidly determine optimal platform assignments
3Measurement precision
If the system assumes best options are static, then the decision-making is simple, but it fails to capture platform variations over time
Solution Approach 1:
The system implements periodic re-evaluation of crowdsourcing platforms by collecting performance data at regular intervals and updating distribution weights accordingly. This periodic action ensures that the evaluation captures temporal variations in platform performance while maintaining a structured approach to monitoring changes
Data Source
AI summary
A method and system for managing allocation of tasks to a plurality of crowdsourcing arms is disclosed. The method includes distributing a set of tasks to the plurality of crowdsourcing arms based on a predefined condition. In response to the distributing, a verification data corresponding to the plurality of crowdsourcing arms is received after a predefined interval. The predefined condition is then updated based on the verification data received. Further, the set of tasks among the plurality of crowdsourcing arms are redistributed based on the updated predefined condition.