Patents by Inventor Sara Amini

Sara Amini 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).

  • 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
  • Publication number: 20230135619
    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 geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Application
    Filed: October 7, 2022
    Publication date: May 4, 2023
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini
  • 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
  • Patent number: 11615271
    Abstract: 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 configur
    Type: Grant
    Filed: June 3, 2020
    Date of Patent: March 28, 2023
    Assignee: Cerebri AI Inc.
    Inventors: Eyal Ben Zion, Alain Charles Briancon, Pranav Mahesh Makhijani, Thejas Narayana Prasad, Sara Amini, Jian Deng, Ngoc Thu Nguyen, Jean Joseph Belanger
  • Patent number: 11501213
    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 geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Grant
    Filed: May 6, 2020
    Date of Patent: November 15, 2022
    Assignee: Cerebri AI Inc.
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini
  • Publication number: 20200380416
    Abstract: 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 configur
    Type: Application
    Filed: June 3, 2020
    Publication date: December 3, 2020
    Inventors: Eyal Ben Zion, Alain Charles Briancon, Pranav Mahesh Makhijani, Thejas Narayana Prasad, Sara Amini, Jian Deng, Ngoc Thu Nguyen, Jean Joseph Belanger
  • Publication number: 20200356900
    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 geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
    Type: Application
    Filed: May 6, 2020
    Publication date: November 12, 2020
    Inventors: Alain Charles Briancon, Eyal Ben Zion, Sumant Sudhir Kawale, Sara Amini
  • 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