Crowdsourcing Worker Selection via Collaboration Graphs
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Solution Overview
Problem
Crowdsourcing platforms face uncertainty regarding the availability of workers who meet Service Level Agreements (SLAs) for tasks, affecting turn-around-times and the inability of requestors to select suitable workers for tasks.
Innovation Solution
A method and system that identify suitable workers based on their performance history and SLAs, generating graphs to visualize worker connections and task characteristics, allowing requestors to select workers and workers to select tasks dynamically, facilitating direct communication for SLA negotiations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workers are selected based on performance history and SLAs using graph-based visualization, then task completion efficiency and SLA adherence are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent introduces a crowdsourcing platform server as an intermediary between requestors and workers. The server generates graph representations that visualize worker availability, performance metrics, and SLA compliance history. This intermediary processing resolves the contradiction by centralizing the complex computational tasks of evaluating worker suitability while providing simplified visual interfaces to users, thereby improving productivity without exposing the underlying system complexity to end users.
Solution Approach 2:
The patent transforms worker performance data from traditional tabular or list formats into graph-based visual representations. By adding visual dimensions (nodes representing workers, edges representing collaborations or performance relationships), the system enables requestors to quickly assess worker suitability across multiple criteria simultaneously. This dimensional transformation improves task completion efficiency by making complex performance histories visually accessible without increasing apparent system complexity to users.
2Ease of operation
If the system provides detailed worker performance graphs and multiple selection parameters, then requestor ability to select suitable workers is improved, but information processing time and interface complexity increase
Solution Approach 1:
The system pre-computes and stores worker performance metrics, SLA compliance histories, and collaboration graphs before requestors need them. When a requestor views available workers, the graph data is already prepared and rendered, eliminating real-time computation delays. This preliminary action enables detailed worker selection capabilities without adding processing time to the user interaction flow.
Solution Approach 2:
The patent creates visual copies or representations of worker performance data in graph format. Instead of requiring requestors to analyze raw performance data or navigate through multiple screens of worker information, the system generates graphical copies that consolidate multiple data dimensions (performance history, SLA adherence, collaboration patterns) into single visual elements. This copying approach improves ease of operation by presenting comprehensive worker information in an easily consumable format without increasing information processing time.
3Reliability
If the platform tracks and visualizes worker collaboration patterns through graph edges, then task assignment quality is improved, but data collection and processing requirements increase
Solution Approach 1:
The graph data structure serves multiple functions simultaneously: it tracks worker performance metrics, records collaboration patterns through edges, visualizes worker availability, and supports SLA compliance verification. By making the graph structure universal and multi-functional, the system improves task assignment quality through comprehensive worker profiling without proportionally increasing data collection requirements. The same collaborative edge data that shows worker relationships also informs task assignment recommendations.
Data Source
AI summary
The disclosed embodiments illustrate methods and systems for crowdsourcing a task. The method includes identifying a first set of workers from workers, based on a performance of the workers on a set of tasks previously attempted by the workers, and a Service Level Agreement (SLA) associated with the task. The method further includes generating a graph comprising nodes and edges connecting the nodes. Each of the one or more nodes is indicative of a worker. An edge, connecting two workers, is indicative of said two workers having worked together on at least one task. The method further includes receiving an input, to select a second set of workers from the first set of workers, based on one or more first parameters associated with each of the nodes in said graph and second parameters associated with each of the edges in the graph.