Hybrid Annotation Framework for Industry-Specific Label Accuracy
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Solution Overview
Problem
Existing automated data annotation systems lack industry-specific accuracy due to a lack of vernacular and usage know-how, leading to insufficiently precise labeling for industry-specific applications.
Innovation Solution
A hybrid approach combining automated annotation with manual review and edit processes, utilizing cognitive intelligence solutions and industry-specific knowledge to enhance the accuracy of metadata generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated annotation processes are used, then processing efficiency is improved, but labeling accuracy for industry-specific applications deteriorates
Solution Approach 1:
The patent combines automated annotation processes with manual review processes into a hybrid system. The automated process handles initial annotation to maintain efficiency, while manual reviewers with industry-specific expertise verify and correct the annotations to ensure accuracy. This merging of automated and manual processes resolves the contradiction between processing efficiency and labeling accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between automated annotation and final output, where industry experts review and validate the automated annotations. This intermediary manual review process acts as a mediator that ensures industry-specific accuracy while allowing the automated process to handle the bulk of annotation work, thus maintaining both efficiency and accuracy.
2Measurement precision
If manual annotation processes are used, then labeling accuracy is improved, but processing time and labor costs increase
Solution Approach 1:
The patent applies partial manual action by having industry experts review only a subset of annotations generated by the automated process, rather than manually annotating all data. This partial manual intervention maintains high accuracy for critical cases while allowing the automated process to handle the majority of annotations, thus reducing overall processing time and labor costs.
Solution Approach 2:
The automated annotation system serves itself by generating initial annotations that are then validated by manual reviewers. The system self-corrects through the feedback loop where manual review results are used to improve and retrain the automated annotation models, reducing the need for extensive manual annotation over time.
3Speed
If automated annotation systems are used, then processing speed is improved, but industry-specific knowledge accuracy deteriorates
Solution Approach 1:
The patent performs preliminary automated annotation at high speed to generate initial labels, followed by subsequent manual review to ensure industry-specific knowledge accuracy. This preliminary action allows the system to quickly process large volumes of data while maintaining reliability through the follow-up expert validation.
Solution Approach 2:
The system implements a feedback mechanism where manual review results are fed back into the automated annotation process to continuously improve and retrain the models. This feedback loop ensures that industry-specific knowledge accuracy is maintained and improved over time while preserving the speed benefits of automated processing.
Data Source
AI summary
A computer-implemented method that includes a programmatically configured annotation processor that may include a processing engine for ingesting using an orchestrated solution that includes a plurality of data objects of one or more data formats. The annotation processor may further identify, using an orchestrated annotation recognition engine, one or more attributes of a data object. The orchestrated annotation recognition engine is configured to determine attribute data from a data object. The data objects are further classified by one or more attributes for associating at least one data object into one or more data sets of annotation data and metadata wherein the annotation data is based on the metadata. The annotation model is generated, based on at least a classified data set of the annotation data and the metadata. The annotation model is configured using the annotation data and metadata wherein the annotation data is created by industry-specific input.