Cerebri Ai Inc.

Sara Amini

Alain Charles Briancon

Eyal Ben Zion

David Alexander Curry

Michael Henry Engeling

Dmitrii Aleksandrovich Boldyrev

Sathish Kumar Lakshmipathy

Jean Joseph Belanger

Thejas Narayana Prasad

Pranav Mahesh Makhijani

Sumant Sudhir Kawale

Ngoc Thu Nguyen

Jian Deng

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

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

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

Machine learning pipeline optimization

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 geographic locale in the future; and storing the training the trained predictive machine learning model in memory.

Predictive, machine-learning, locale-aware computer models suitable for location- and trajectory-aware training sets

Growing community of inventors

Fairfax Station, VA, United States of America

Sara Amini