Crowd Sourcing Worker Selection Using Functional Element Matching
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Solution Overview
Problem
Current methods for assigning workers to projects in crowd sourcing are inefficient, as they rely on predetermined classification items that may not accurately match workers to projects with unique features, requiring frequent updates and external evaluation factors, and are cumbersome to manage.
Innovation Solution
A method that identifies functional elements of a project, extracts worker history, calculates a matching value based on average working hours and project tool usage, and selects workers based on this value, allowing for real-time adjustments to the worker pool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If projects are classified into predetermined items (template type, difficulty level, work type), then worker matching can be performed using established criteria, but the system requires frequent updates to add new items when project features slightly deviate from existing categories
Solution Approach 1:
The patent segments the project classification into functional elements (work tools) instead of using predetermined categorical items. Each project is broken down into specific functional components such as image processing, text processing, audio processing, etc., allowing flexible combination without requiring new category creations.
Solution Approach 2:
The patent changes the parameter of classification from categorical items (type A, B, C) to functional elements with measurable attributes (working hour, number of uses). This allows continuous evaluation and matching based on quantitative parameters rather than discrete categories.
2Measurement precision
If external evaluation factors are included when no previous projects match the current project, then worker matching accuracy improves, but the evaluation process becomes more complex and time-consuming
Solution Approach 1:
The system performs self-service by automatically extracting functional elements and calculating matching scores based on worker history with those same functional elements. The evaluation process uses the same criteria (working hours, usage counts) for both project decomposition and worker assessment, eliminating the need for separate external evaluation factors.
Solution Approach 2:
The patent creates a universal evaluation framework where the same functional element extraction and matching score calculation applies to all projects regardless of type. This single methodology handles both exact matches and cases requiring external evaluation factors, simplifying the overall process.
3Ease of operation
If predetermined classification items are used for project assignment, then the matching process is straightforward, but manual updates are required frequently to maintain accuracy
Solution Approach 1:
The patent replaces the manual mechanical process of updating classification items with an automated system that extracts functional elements directly from project descriptions and calculates matching scores algorithmically. This substitution eliminates manual intervention entirely.
Solution Approach 2:
The system performs preliminary extraction of functional elements from project descriptions before the matching process begins. By pre-identifying the functional components and their attributes, the system prepares all necessary data in advance, eliminating the need for manual updates during the assignment process.
4Measurement precision
If functional elements are identified and matching values are calculated based on working hours and tool usage, then worker selection accuracy improves, but the calculation process requires more data processing
Solution Approach 1:
The patent applies partial action by focusing calculations only on the specific functional elements relevant to each project rather than evaluating all possible worker attributes. This selective approach maintains precision while reducing unnecessary computational overhead.
Data Source
AI summary
A worker selection method according to characteristics of a crowdsourcing-based project is provided. The worker selection method according to characteristics of a crowdsourcing-based project comprises the steps of: identifying functional elements included in a project scheduled to open; extracting a worker's participation history in a previous project; extracting functional elements included in the previous project of the worker; extracting average working hours for each functional element with respect to the functional elements included in the project scheduled to open, among the functional elements included in the previous project; calculating a matching value of the worker for the project scheduled to open, on the basis of the extracted average working hours for each functional element and the number of tasks which have been performed using each functional element in the previous project by the worker; and selecting the worker on the basis of the calculated matching value of the worker for the project scheduled to open.