Patents by Inventor René Bonvanie

René Bonvanie 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).

  • MULTI-MODEL BASED TARGET ENGAGEMENT SEQUENCE GENERATOR
    Publication number: 20230011066
    Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.
    Type: Application
    Filed: September 15, 2022
    Publication date: January 12, 2023
    Inventors: Jere Armas Michael Helenius, Nandan Gautam Thor, Erik Michael Bower, René Bonvanie
  • Multi-model based target engagement sequence generator
    Patent number: 11494610
    Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.
    Type: Grant
    Filed: March 31, 2019
    Date of Patent: November 8, 2022
    Assignee: Palo Alto Networks, Inc.
    Inventors: Jere Armas Michael Helenius, Nandan Gautam Thor, Erik Michael Bower, René Bonvanie
  • MULTI-MODEL BASED TARGET ENGAGEMENT SEQUENCE GENERATOR
    Publication number: 20200311513
    Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.
    Type: Application
    Filed: March 31, 2019
    Publication date: October 1, 2020
    Inventors: Jere Armas Michael Helenius, Nandan Gautam Thor, Erik Michael Bower, René Bonvanie