Cerebri Ai Inc.

Alain Briançon

Gabriel M Silberman

Michael Thomas Wegan

Gregory Klose

Lee David Harper

Andrew Kraemer

Arun Prakash

Jean Joseph Belanger

James Stojanov

Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.

Machine-learning models to leverage behavior-dependent processes

Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.

Monitoring and controlling continuous stochastic processes based on events in time series data

Multi-stage machine-learning models to control path-dependent processes

Growing community of inventors

Germantown, MD, United States of America

Alain Briançon