Schema-Based Data Payload Conversion for ML Training Suitability
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Solution Overview
Problem
Converting data payloads with different formats to a common format for processing is time-consuming and labor-intensive, and determining their suitability for training machine learning models is difficult.
Innovation Solution
Convert data payloads to a schema, generate feature vectors, and determine suitability based on cosine similarity for training machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If data payloads with different formats are converted to a common format manually, then data processing consistency is improved, but time consumption and labor intensity increase
Solution Approach 1:
The patent replaces manual mechanical conversion processes with an automated machine learning-based system. The system uses a transformer model that automatically learns to convert data payloads between different formats by analyzing examples, eliminating the need for manual conversion rules and reducing both time consumption and labor intensity while maintaining processing consistency.
Solution Approach 2:
The patent changes the approach from static format conversion to dynamic adaptive conversion. The system learns conversion patterns from example data and automatically adjusts its behavior based on the specific data payloads received, enabling flexible format transformation without predefined rigid rules, thus reducing conversion time while maintaining consistency.
2Manufacturing precision
If manual conversion rules are used for different data formats, then conversion accuracy is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual conversion rule systems with a simplified machine learning model. Instead of maintaining extensive if-else logic and conversion tables, the system uses a transformer model that learns conversion patterns from examples, reducing system complexity while maintaining or improving conversion accuracy through intelligent pattern recognition.
Solution Approach 2:
The system performs self-learning from example data payloads, automatically generating conversion capabilities without requiring manual programming of conversion rules. This self-service approach reduces system complexity by allowing the model to adapt to different formats autonomously based on the training examples provided.
3Measurement precision
If data suitability for training is manually assessed, then assessment accuracy is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual suitability assessment with an automated machine learning-based evaluation system. The system uses the same transformer model that handles conversion to also assess data suitability for training by analyzing feature vectors and determining quality metrics automatically, reducing assessment time while maintaining or improving accuracy through consistent algorithmic evaluation.
Data Source
AI summary
Technologies are provided for determining a suitability of data payloads for training a machine learning model. A schema can be generated based on sample data payloads that have different data formats. The sample data payloads (and/or additional data payloads) can be converted to a format that conforms to the schema. Feature vectors can then be generated based on the converted data payloads, and used to determine a suitability of the data payloads for training a machine learning model. If the data payloads are sufficiently suitable, the converted data payloads can be used to train the machine learning mode. Otherwise, the schema may be annotated and new converted payloads may be generated based on the annotated schema. The feature vector generation and suitability analysis can then be repeated.