Training machine learning models for interest prediction

This invention describes a way to train a computer model that can predict what a person is interested in. It works by gathering various types of data about individuals, such as their known interests, observational data, and specific categorical information like cognitive development markers. This data is then used to create a training dataset, where different pieces of information are weighted based on observations, to teach the model how to make accurate interest predictions. The data can be collected from both physical and digital environments, including electronic communications.

Why it matters: Filed before the widespread availability of advanced multimodal AI. The claims cover processing diverse data, including electronic communication and physical observations, which is now more efficiently handled by current AI models capable of understanding complex human behavior and preferences.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedMachine learning system, data collection infrastructure

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