Grammar-Based Synthetic Form Data Generation for ML Training
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Solution Overview
Problem
The implementation of machine learning techniques is hindered by the complexity of algorithms, the resource-intensive process of generating and updating models, and the need for substantial high-quality labeled training data, which is time-consuming and prone to errors, making it difficult to adapt models to changing environments.
Innovation Solution
A grammar-based automated system generates annotated synthetic form training data, allowing for the creation of large amounts of high-quality training data without human intervention, using a training data generation engine that produces layouts and key-value units based on grammatical rules, enabling faster model training and improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human annotation is used to create training data, then high-quality labeled data can be obtained, but the process is extremely time-consuming and resource-intensive
Solution Approach 1:
The patent uses synthetic data generation to create copies of real-world form documents through grammar-based templates. Instead of manually annotating each real document, the system generates synthetic forms that replicate the structure and characteristics of real forms, then automatically annotates them. This copying approach maintains annotation quality while eliminating the time-consuming manual process.
Solution Approach 2:
The system implements self-service annotation through automated grammar-based form generation. The generation engine automatically creates forms and their annotations without human intervention, making the annotation process self-sufficient. This eliminates the need for manual human annotation while maintaining consistent, high-quality labeled data.
2Reliability
If substantial amounts of high-quality labeled training data are collected manually, then model training accuracy improves, but the resource consumption and time requirements increase dramatically
Solution Approach 1:
The patent generates synthetic training data by creating grammatical copies of real form structures. The system produces large volumes of synthetic forms that mirror real-world document patterns, enabling model training with high-quality data without the resource-intensive manual collection process. This copying mechanism maintains data reliability while dramatically improving productivity.
Solution Approach 2:
The system performs preliminary action by pre-generating large datasets of synthetic annotated forms before model training begins. The grammar-based generation engine creates and annotates forms in advance, building a ready-to-use training corpus that eliminates the need for time-consuming manual data collection during the modeling process.
3Measurement precision
If machine learning models are trained on real-world data, then they achieve high accuracy, but the process is extremely focused on particular use cases and requires complete regeneration when environments change
Solution Approach 1:
The patent implements universality through grammar-based form generation templates that can produce multiple types of forms across different domains. The same generation engine and grammatical framework can create various form structures by modifying templates and parameters, enabling a single model to adapt to different use cases without complete regeneration. This multi-functional approach maintains accuracy while improving adaptability.
Solution Approach 2:
The system applies dynamics by making the training data generation process adaptable and flexible. The grammar-based templates can be dynamically modified to reflect changes in form structures and requirements. When environments or use cases change, the generation engine can update templates and produce new synthetic training data accordingly, allowing models to adapt without complete regeneration while maintaining high accuracy.
4Measurement precision
If manual annotation processes are used to create training data, then data quality can be maintained, but the process is prone to human errors and requires extensive human intervention
Solution Approach 1:
The patent implements self-service through automated grammar-based form generation and annotation. The system automatically generates forms and their annotations without human intervention, eliminating human errors while maintaining consistent quality. The generation engine serves itself by creating and labeling synthetic data autonomously, achieving high automation levels without sacrificing annotation accuracy.
Solution Approach 2:
The system uses copying to replicate real form structures through grammatical templates, creating synthetic data that can be automatically annotated. This copying process eliminates the need for manual human annotation, removing the source of human errors while maintaining annotation accuracy through rule-based automatic labeling of synthetic forms.
Data Source
AI summary
Techniques for grammar-based automated generation of annotated synthetic form training data for machine learning are described. A training data generation engine utilizes a defined grammar to construct a layout for a form, select key-value units to place within the layout, and select attribute variants for the key-value units. The form is rendered and stored at a storage location, where it can be provided along with other similarly-generated forms to be used as training data for a machine learning model.