Automated Data Annotation System for Machine Learning Model Training
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Solution Overview
Problem
The manual annotation of large datasets for machine learning models is time-consuming, resource-intensive, and prone to errors, limiting the efficiency and accuracy of training data creation.
Innovation Solution
An automated system using Application Programming Interfaces (APIs) to generate annotated training data by identifying and labeling data samples, conforming to a specified format, which can be used directly by machine learning model training services, thereby reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual annotation is used to create training data, then human judgment and flexibility are maintained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service annotation where the machine learning model automatically annotates data samples using its own trained capabilities. The model processes data independently without requiring human annotators, thereby maintaining high productivity while the automated system adapts to different data types and formats through its learned patterns.
Solution Approach 2:
The patent replaces the mechanical human annotation process with an automated computer system. Instead of humans manually reviewing and labeling data, the system uses algorithmic processing to automatically identify patterns, classify data, and generate annotations, thereby eliminating the time-consuming manual labor while maintaining consistent annotation quality.
2Ease of operation
If manual annotation is used to create training data, then flexibility in handling complex cases is maintained, but the process is prone to errors
Solution Approach 1:
The system incorporates feedback mechanisms where the annotated data is used to retrain and refine the machine learning model. This continuous feedback loop allows the model to learn from its annotations, correct errors, and improve its annotation accuracy over time, thereby enhancing reliability while maintaining the ability to handle complex data patterns.
Solution Approach 2:
The patent replaces error-prone manual annotation with automated algorithmic processing that maintains consistent accuracy. The system uses structured decision trees, pattern recognition algorithms, and validation mechanisms to ensure reliable annotations, eliminating the variability and errors inherent in human judgment while preserving the ability to handle complex cases through learned patterns.
3Productivity
If automated annotation is implemented, then efficiency and consistency are improved, but system complexity increases
Solution Approach 1:
The system achieves universality by designing a single automated annotation platform that can handle multiple data types, formats, and annotation tasks. The machine learning model is trained to perform various annotation functions across different domains, thereby improving efficiency and consistency without requiring separate complex systems for each specific annotation task.
Solution Approach 2:
The patent applies preliminary action by pre-training the machine learning model on extensive labeled data before it is used for actual annotation tasks. This preliminary training phase equips the system with the necessary patterns and knowledge, allowing it to efficiently and accurately annotate new data without requiring complex real-time decision-making logic during the annotation process itself.
Data Source
AI summary
Systems and methods provide reception of a plurality of data samples for training a machine learning model and a plurality of examples associated with each of a plurality of ground truth labels for training a machine learning model, identification of all examples of the plurality of examples within each of the data samples, determination, for each identified example, of an associated one of the plurality of labels and a location of the example in the data sample, annotation of the data sample with the associated one of the plurality of labels and the location, and training of a machine learning model using the annotated data sample.