Patents by Inventor Dmitrii Aleksandrovich Boldyrev

Dmitrii Aleksandrovich Boldyrev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Predictive, machine-learning, event-series computer models with encoded representation
    Patent number: 12165025
    Abstract: 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.
    Type: Grant
    Filed: March 7, 2023
    Date of Patent: December 10, 2024
    Assignee: Cerebri AI Inc.
    Inventors: Sathish Kumar Lakshmipathy, Eyal Ben Zion, David Alexander Curry, Alain Charles Briancon, Michael Henry Engeling, Dmitrii Aleksandrovich Boldyrev, Sara Amini
  • PREDICTIVE, MACHINE-LEARNING, EVENT-SERIES COMPUTER MODELS WITH ENCODED REPRESENTATION
    Publication number: 20230222387
    Abstract: 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.
    Type: Application
    Filed: March 7, 2023
    Publication date: July 13, 2023
    Inventors: Sathish Kumar Lakshmipathy, Eyal Ben Zion, David Alexander Curry, Alain Charles Briancon, Michael Henry Engeling, Dmitrii Aleksandrovich Boldyrev, Sara Amini
  • Predictive, machine-learning, time-series computer models suitable for sparse training sets
    Patent number: 11636393
    Abstract: 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.
    Type: Grant
    Filed: May 6, 2020
    Date of Patent: April 25, 2023
    Assignee: Cerebri AI Inc.
    Inventors: Sathish Kumar Lakshmipathy, Eyal Ben Zion, David Alexander Curry, Alain Charles Briancon, Michael Henry Engeling, Dmitrii Aleksandrovich Boldyrev, Sara Amini
  • PREDICTIVE, MACHINE-LEARNING, TIME-SERIES COMPUTER MODELS SUITABLE FOR SPARSE TRAINING SETS
    Publication number: 20200356878
    Abstract: 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.
    Type: Application
    Filed: May 6, 2020
    Publication date: November 12, 2020
    Inventors: Sathish Kumar Lakshmipathy, Eyal Ben Zion, David Alexander Curry, Alain Charles Briancon, Michael Henry Engeling, Dmitrii Aleksandrovich Boldyrev, Sara Amini