Automated Annotation Engine for Machine Learning Data
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Solution Overview
Problem
Current machine learning systems, particularly neural networks, require extensive human intervention and resources for data annotation, which is time-consuming and prone to errors, especially when dealing with changes in product appearance or the introduction of new products, leading to inefficiencies in training and potential misclassifications.
Innovation Solution
An automated annotation engine that uses data capture logic, association logic, and annotator logic to create annotations for machine learning datasets, enabling the system to detect discrepancies and emit alerts, and incorporates known identifiers to enrich annotations, thereby reducing human error and improving the efficiency of the annotation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation by human operators is used, then training data can be created, but the process is time-consuming and prone to human error
Solution Approach 1:
The system uses automated annotation logic that operates without human intervention to create training annotations. The annotation logic processes captured data and automatically generates annotations, eliminating the need for manual human annotation while maintaining consistency and reducing errors.
Solution Approach 2:
The patent replaces the mechanical process of manual human annotation with an automated computational system. The annotation logic uses algorithms and processing rules to generate annotations automatically, substituting human cognitive and manual processes with machine-based automation.
2Reliability
If extensive human intervention is used for data annotation, then training quality can be maintained, but resource consumption increases
Solution Approach 1:
The automated annotation system operates independently without requiring human operators for each annotation task. The system self-manages the annotation process by capturing data, processing it through annotation logic, and generating training annotations automatically, thereby improving efficiency while maintaining reliability through consistent automated processes.
3Measurement precision
If the training process is repeated for product changes, then model accuracy is maintained, but time consumption increases
Solution Approach 1:
The system captures and processes data in advance to create and update training annotations automatically. By maintaining an up-to-date training dataset through automated annotation, the system is prepared for product changes without requiring repeated manual retraining processes, thus improving adaptability while maintaining accuracy.
4Adaptability or versatility
If manual annotation processes are used, then new products can be added to the training set, but the process is resource intensive
Solution Approach 1:
The automated annotation system handles new product integration independently. When new products are introduced, the system automatically captures their data, processes it through the annotation logic, and generates appropriate annotations without requiring human operators, thereby improving throughput while maintaining adaptability to new products.
Data Source
AI summary
Provided is machine learning apparatus comprising: a dataset for input to a training procedure of a machine learning model; data capture logic operable to capture from an object at least one datum for inclusion in the dataset; association logic operable to derive an additional characteristic of the object; annotator logic operable in response to the data capture logic and the association logic to create an annotation linking the additional characteristic with the at least one datum; storage logic operable to store the or each datum with an associated annotation in the dataset; and input logic to supply the dataset as machine learning input.