Hybrid Annotation Framework for Industry-Specific Labeling 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 of data objects for industry-specific applications.
Innovation Solution
A hybrid approach combining automated annotation processes with manual review and edit processes to generate an annotation model, utilizing cognitive intelligence solutions and industry-specific knowledge to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated annotation processes are used, then productivity is improved, but manufacturing precision deteriorates due to lack of industry-specific knowledge
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 productivity, while the manual review process corrects inaccuracies to improve precision. This merging of automated and manual workflows resolves the contradiction by allowing both speed and accuracy to coexist.
Solution Approach 2:
The system implements feedback loops where manually reviewed and corrected annotations are fed back into the automated annotation model for retraining. This feedback mechanism allows the automated system to learn from human expertise and improve its precision over time while maintaining high productivity through automation.
2Manufacturing precision
If manual review and edit processes are added to automated annotation, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The annotation system is segmented into distinct modular components: an automated annotation module, a manual review module, and a feedback integration module. This segmentation allows each component to perform its specific function independently, making the overall complex system more manageable and maintainable while achieving improved precision through the coordinated work of these specialized modules.
3Manufacturing precision
If industry-specific knowledge is integrated into annotation models, then manufacturing precision is improved, but loss of time increases due to manual intervention
Solution Approach 1:
The system performs preliminary automated annotation before manual review, so that the bulk of the annotation work is completed automatically using pre-trained models. This preliminary action reduces the time required for manual intervention to only the correction and refinement phase, thereby minimizing time loss while still achieving high precision through the integration of industry-specific knowledge in the manual review stage.
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.