Computer-implemented adaptive subscription models for consumer packaged goods

This invention describes a system that helps manage subscriptions for consumer goods. It works by collecting detailed information about users and their households, including what they consume, to build a database. This data is then used to train a computer model that predicts how much of different goods a household will need, specifically by figuring out how household characteristics influence consumption and identifying when a particular characteristic causes a change in consumption.

Why it matters: Filed before the widespread availability of more accessible MLOps tools and explainable AI frameworks. These now simplify the iterative training and identification of specific consumption drivers claimed.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedHeavy software system, machine learning models

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