Dynamic Annotation Validation Using State-Label Mapping and Comparative ANN
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Solution Overview
Problem
The manual process of annotating and validating large datasets for machine learning models is time-consuming, error-prone, and requires significant effort, making it necessary to develop a more efficient method for generating and verifying quality-labelled datasets.
Innovation Solution
A system and method utilizing state-label mapping models and comparative artificial neural network (ANN) models, such as Siamese CNN, to dynamically annotate and validate data points by generating annotations or receiving and validating annotations from external devices or users, based on verified annotated training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation is performed by users, then annotation quality can be maintained, but time consumption and effort increase significantly
Solution Approach 1:
The patent introduces an automated validation system that acts as an intermediary between manual annotators and the final labelled dataset. This system uses machine learning models to pre-validate annotations before human review, filtering out obvious errors and providing suggestions to annotators, thereby reducing the time required for manual validation while maintaining quality standards.
Solution Approach 2:
The patent implements a self-service annotation system where the system automatically generates annotations using pre-trained models, and annotators only need to review and correct them when necessary. This reduces the overall manual effort required while maintaining annotation quality, as the system handles routine annotation tasks autonomously.
2Reliability
If manual validation of annotations is performed, then annotation errors can be detected, but the process becomes highly time-consuming
Solution Approach 1:
The patent replaces the mechanical process of manual validation with an automated computer-based validation system. This system uses machine learning models trained on verified annotated training data to automatically detect annotation errors, replacing the need for extensive manual review while maintaining high error detection capability and significantly improving validation speed.
Solution Approach 2:
The patent performs preliminary validation of annotations using automated models before final human review. This pre-validation step identifies and flags potential errors in advance, allowing human validators to focus only on uncertain cases, thereby reducing overall validation time while maintaining thorough error detection.
3Manufacturing precision
If huge labelled datasets are verified manually, then data quality can be ensured, but the effort and time required become unsustainable
Solution Approach 1:
The patent divides the verification process into multiple stages: automated preliminary validation using machine learning models, intermediate review of uncertain cases, and final quality assurance. This segmentation allows the system to handle huge datasets efficiently by distributing the verification workload across different automated and manual steps, reducing overall complexity while maintaining data quality.
Solution Approach 2:
The patent creates a multi-functional verification system that can handle different types of annotations, data formats, and quality requirements using a unified automated validation framework. This universal system reduces the complexity of verifying huge datasets by providing a single scalable solution rather than requiring separate manual verification processes for each dataset.
Data Source
AI summary
This disclosure relates to method and system for of dynamically annotating data or validating annotated data. The method may include receiving input data comprising a plurality of input data points. The method may further include one of: a) generating a plurality of annotations for each of the plurality of input data points using at least one of a state-label mapping model and a comparative ANN model, or b) receiving the plurality of annotations for each of the plurality of input data points from an external device or from a user, and validating the plurality of annotations using at least one of the state-label mapping model and the comparative artificial neural network (ANN) model.