Machine learning-based data object storage

This invention describes a system that uses machine learning to intelligently decide which data to move to a secondary storage device and when to do it. Instead of relying on fixed rules, the system learns from how data is used on a computer. It then uses this learned intelligence to predict future storage needs, automatically optimizing what gets stored where and when. The claims focus on the system's ability to train and retrain this machine learning model using various data sources, including user activity.

Why it matters: Filed in 2020, the intervening years have seen significant advancements in accessible machine learning frameworks and cloud-based ML services. This makes the implementation and deployment of the predictive model training and retraining, as described in the claims, more feasible and less resource-intensive today.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, ML model training, networked components

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