Dataset Annotation System Using Gamified Micro-Task Distribution
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Solution Overview
Problem
Current dataset annotation methods for machine learning models are costly, inefficient, and limited by the skill set and number of human annotators, leading to high costs and reduced annotation quality and throughput.
Innovation Solution
A system and method that utilizes existing in-game and in-app advertising systems to distribute annotation tasks to a large user base with diverse skill sets, providing incentives for task completion and utilizing machine learning models for verification and correction, thereby creating a scalable and efficient annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If annotation is outsourced to specialist companies employing large numbers of human annotators, then annotation quality can be maintained, but costs become high and throughput is limited by the number of employees
Solution Approach 1:
The patent divides annotation projects into multiple micro-tasks that can be distributed to a large pool of gamers. Each micro-task is independent and can be completed by different users, enabling parallel processing and significantly increasing throughput while maintaining quality through the aggregation of many individual contributions
Solution Approach 2:
The system creates a multi-functional platform that combines game playing with annotation tasks. The same user base serves dual purposes: enjoying games and contributing to annotation projects, thereby expanding the annotator pool beyond traditional specialist companies and increasing available throughput
2Measurement precision
If multiple annotation companies are used to match specific skill sets, then annotation quality for specific tasks improves, but system complexity increases due to combining results from multiple companies
Solution Approach 1:
The platform creates a universal annotator pool where gamers with diverse skills can be matched to various annotation tasks through a centralized system. This eliminates the need for multiple specialized companies while maintaining skill matching capability, thereby reducing system complexity
Solution Approach 2:
The patent introduces a centralized task distribution and result aggregation system that acts as an intermediary between annotation requests and gamer annotators. This intermediary automatically matches tasks with suitable annotators based on skills and aggregates results, simplifying the coordination that would otherwise require multiple companies
3Loss of energy
If a limited number of employees are used for annotation, then costs are reduced, but annotation throughput is limited by the number of employees
Solution Approach 1:
By segmenting annotation projects into numerous small micro-tasks, the system can distribute work to a large number of low-cost gamer annotators instead of employing few expensive specialists. This segmentation enables cost-effective scaling of throughput by simply adding more annotators to the pool
Solution Approach 2:
The system leverages the self-motivated gamer community who perform annotation tasks as part of their gaming experience, often in exchange for in-game rewards or currency. This self-service model eliminates the need for traditional employment relationships, reducing overhead costs while enabling large-scale participation and high throughput
Data Source
AI summary
A non-transitory computer readable medium for annotating a dataset, the computer readable medium containing instructions that when executed by at least one processor, cause the at least one processor to perform a method, the method including: dividing a dataset to be annotated into annotating tasks by an annotator engine; distributing the annotating tasks to selected users by a distribution server for completion of the annotating tasks; and reassembling the completed annotation tasks into an annotated dataset.