Synthetic Training Data Generation via Embedding Models
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Solution Overview
Problem
Machine learning models, particularly those for behavior prediction and language processing, face challenges due to a lack of high-quality and controllable training data, leading to sub-optimal performance.
Innovation Solution
The system employs a first language processing model to process plain text descriptions and generate representations in a real-valued space, which are then used to train a second model to predict sequences of behavior tokens, thereby creating synthetic data to improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional algorithms are used for behavior prediction, then the system is simpler to implement, but predictive accuracy and performance are sub-optimal
Solution Approach 1:
The patent introduces an embedding model as an intermediary component that transforms text descriptions into numerical representations. This intermediary enables the language processing model to effectively process behavioral data, thereby improving predictive accuracy while maintaining a modular system architecture that manages complexity.
Solution Approach 2:
The patent replaces traditional statistical algorithms with a deep learning-based language processing model. This substitution leverages neural network mechanisms to capture complex patterns in behavioral sequences, significantly improving predictive performance despite increased computational requirements.
2Reliability
If abundant training data is used, then model performance improves, but data quality control becomes more difficult
Solution Approach 1:
The patent employs synthetic data generation by copying and transforming existing data patterns through the embedding model. The system generates artificial training examples that mimic real-world behavioral patterns, thereby expanding training data availability while maintaining controlled quality through the deterministic nature of the generation process.
3Manufacturing precision
If synthetic training data is generated using embedding models, then data quality and controllability improve, but the system complexity increases
Solution Approach 1:
The patent segments the system into distinct functional modules: an embedding model for text representation, a language processing model for sequence prediction, and a training data generation component. This segmentation allows each component to be optimized independently while maintaining overall system manageability and clarity.
4Measurement precision
If language processing models are used for behavior prediction, then predictive error rates decrease, but the lack of clear training frameworks increases implementation difficulty
Solution Approach 1:
The patent implements a feedback mechanism where the language processing model's predictions are evaluated against actual behavioral outcomes. This feedback loop enables iterative training and refinement of the model, gradually reducing predictive error rates while providing a clear training framework through systematic performance monitoring and model updating.
Data Source
AI summary
Methods and systems for generating synthetic data. In some aspects, the system receives a first set of candidate behaviors, each of which comprises plain text. The system processes the first set of candidate behaviors using a first language processing model to generate a set of representations in an embedding space. For each representation in the set of representations, the system processes the representation using a second language processing model to generate a first sequence of behavioral tokens representative of a timeline of user activities. Using the set of representations and first sequence of behavior tokens, the system updates the second language processing model. Using the updated second language processing model, the system processes a second set of candidate behaviors to generate a second sequence of behavior tokens.