Cerebri Ai Inc.

Michael Henry Engeling

Alain Charles Briancon

Jean Joseph Belanger

Pranav Mahesh Makhijani

Gregory Klose

James Cvetan Stojanov

Hongshi Li

Max Changchun Huang

Christopher Michael Coovrey

Mounib Mohamad Ismail

David Alexander Curry

Eyal Ben Zion

Sara Amini

Dmitrii Aleksandrovich Boldyrev

Sathish Kumar Lakshmipathy

Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Predictive, machine-learning, event-series computer models with encoded representation

Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Customer journey management engine

Predictive, machine-learning, time-series computer models suitable for sparse training sets

Growing community of inventors

Austin, TX, United States of America

Michael Henry Engeling