US 12,182,216 B2 · Machine learning model and encoder to predict online user journeys

Abstract

The subject technology identifies a series of journey event types in an online user journey, the event types including an impression event, an email event, a click event, and a website visit, and assigns an encoder to each event type. Using an assigned encoder, the technology encodes each event type to generate an encoded vector for each event type. The encoded vector is representative of at least a portion of the online user journey relating to that event type. The technology generates an encoded vector for each event type to create a set of encoded vectors, the set of encoded vectors including one or more of an impression event encoded vector, an email event encoded vector, a click event encoded vector, and a website visit encoded vector. The technology aggregates the set of encoded vectors to generate an output of the online user journey encoder, the output including a composite encoded user journey vector for modeling, transmits the output of the online user journey encoder to a user journey training model for training of the model and, using a trained model, generates an occurrence probability for at least one further event in the online user journey.

Claims20

Dependent claims

A system for predicting online user activity comprising:

one or more processors;
and
a memory storing instructions which, when executed by at least one processor in the one or more processors, cause the at least one processor to:
identify multiple journey event types in a sample of multiple online user journeys;
assign an encoder to each event type;
use an assigned encoder, encoding each instance of a particular event type in the sample to generate an encoded vector for the particular event type, the encoded vector being representative of at least a portion of one or more of the multiple online user journeys that relates to the particular event type;
generate an encoded vector for each event type to create a set of encoded vectors;
aggregate the set of encoded vectors to generate an output of the online user journey encoder, the output including a composite encoded user journey vector for modeling;
transmit the output of the online user journey encoder to a user journey training model for training of a user journey model;
and
use a trained model, generating an occurrence probability for at least one further event in an online user journey.

8 more elements

Dependencies are inferred from the claim text.

Machine learning model and encoder to predict online user journeys

Life of this patent

Machine learning model and encoder to predict online user journeys

Life of this patent

Abstract

Claims20