MACHINE LEARNING MODEL AND ENCODER TO PREDICT ONLINE USER JOURNEYS

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United States of America Patent

APP PUB NO 20250103664A1
SERIAL NO

18975941

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Abstract

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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.

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Patent Owner(s)

Patent OwnerAddress
ZETA GLOBAL CORP3 PARK AVENUE 33RD FLOOR NEW YORK NY 10016

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Jones, Zachary D Atlanta, US 8 5
Portman, Danny Atlanta, US 20 6

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