Machine Learning Management System for Collaborative Annotation
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Solution Overview
Problem
Existing text markup editors and document tagging tools are difficult for teams to use in collaborative document annotation efforts, as they do not easily integrate with model training technologies and lack automatic prediction and history tracking for analytic tasks.
Innovation Solution
A computer-implemented method and system for machine learning management that receives text data, identifies data features, generates predictive annotations, corrects inaccurate annotations, and monitors progress of annotations made by collaborating users, using a training descriptor to track completion and types of annotations in model training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional text markup editors and document tagging tools are used for collaborative annotation, then basic annotation functionality is provided, but integration with model training technologies is difficult and automatic prediction is lacking
Solution Approach 1:
The patent combines text markup editing, collaborative annotation, and model training functionalities into a single integrated system. The server merges the annotation interface with machine learning model training capabilities, allowing users to perform both annotation and model training without switching between separate tools, thus improving adaptability while managing complexity through unified architecture.
Solution Approach 2:
The annotation system is designed to serve multiple functions: it provides collaborative annotation capabilities, integrates with model training technologies, performs automatic prediction, and enables iterative model improvement. This multi-functional design allows a single system to replace multiple separate tools, enhancing versatility without proportionally increasing complexity.
2Productivity
If manual annotation is performed without automatic prediction, then full control over annotation accuracy is maintained, but annotation productivity is reduced
Solution Approach 1:
The system implements a feedback loop where the machine learning model generates automatic predictions, users review and correct these predictions, and the corrected annotations are used to retrain and improve the model. This iterative feedback process enables the system to maintain high annotation accuracy while significantly improving productivity, as the model progressively learns from user corrections and requires less manual intervention over time.
Solution Approach 2:
The system performs preliminary automatic prediction before user review, providing users with pre-processed annotations that are already partially completed. This preliminary action reduces the manual workload significantly while maintaining accuracy, as users only need to review and correct the automated predictions rather than creating annotations from scratch.
3Ease of operation
If collaborative annotation is enabled without progress tracking, then multiple users can annotate simultaneously, but annotation progress and completion status cannot be monitored
Solution Approach 1:
The system provides real-time feedback to users about their individual progress and the overall team's annotation status. The server monitors and tracks annotation progress, providing users with information about completion status, which helps maintain motivation and enables effective project management while preserving all progress information.
4Measurement precision
If iterative model retraining is performed, then model accuracy progressively improves, but training time and computational resources increase
Solution Approach 1:
The system performs partial retraining by focusing only on the specific data segments that have been corrected by users, rather than retraining the entire model from scratch each time. This selective approach maintains model accuracy improvements while significantly reducing the computational time and resources required compared to full retraining cycles.
Data Source
AI summary
According to one aspect, a method for machine learning management is provided. In one embodiment, the method includes receiving a first segment of text data, identifying data features corresponding to a sequence of characters in the first segment of text data, and generating predictive annotations to the sequence of characters based at least in part on the identified data features. The method can also include identifying inaccurate annotations generated according to the predictive annotations, correcting the identified inaccurate annotations, generating one or more sets of model training data incorporating the corrected annotations, and monitoring progress of annotations made to a second segment of text data associated with the first segment of text data by a plurality of collaborating users of a plurality of managed computers.