Foundation Model Training for Physiological Signals

The patent applies preliminary action by pre-training foundation models on large-scale unlabeled physiological signal data before fine-tuning on smaller labeled datasets. This pre-training phase prepares the models with general physiological patterns, reducing the time and cost required for subsequent labeled data collection and annotation while maintaining high prediction accuracy.

The patent implements self-service through self-supervised learning approaches where models learn from unlabeled data without requiring manual annotation. The models automatically generate their own training signals from the raw physiological signals, eliminating the need for expensive and time-consuming medical expert annotation while still achieving high accuracy.

The patent uses preliminary action by pre-training models on diverse unlabeled physiological data from multiple population demographics before fine-tuning. This preliminary exposure to varied data distributions enhances model generalizability across different populations while avoiding the computational burden of collecting and processing equally large labeled datasets for each demographic.

The patent applies universality by developing foundation models that learn universal physiological patterns from unlabeled data across different devices, populations, and signal types. These universal representations can be adapted to multiple downstream tasks and demographic groups without requiring separate large-scale labeled training for each, reducing overall computational resource requirements.

The patent implements self-service through self-supervised learning where models automatically learn from raw, uncurated physiological signals without requiring manual data curation or annotation. The models generate their own training objectives from the unlabeled data, eliminating the complex data curation process while maintaining reliable predictive capabilities for health conditions.

The patent applies preliminary action by pre-training foundation models on large volumes of uncurated physiological data to learn robust feature representations. This preliminary training on diverse uncurated data provides a strong foundation that can be subsequently fine-tuned for specific health condition predictions, reducing the need for extensive manual data curation while improving predictive reliability.