Patents by Inventor Kiersten Henderson

Kiersten Henderson 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).

  • Prediction, planning, and optimization of trip time, trip cost, and/or pollutant emission for a vehicle using machine learning
    Patent number: 11200358
    Abstract: A method of predicting, in real-time, a relationship between a vehicle's engine speed, trip time, cost, and fuel consumption, comprising: monitoring vehicle operation over time to acquiring data representing at least a vehicle location, a fuel consumption rate, and operating conditions; generating a predictive model relating the vehicle's engine speed, trip time, and fuel consumption; and receiving at least one constraint on the vehicle's engine speed, trip time, and fuel consumption, and automatically producing from at least one automated processor, based on the predictive model, a constrained output.
    Type: Grant
    Filed: September 3, 2020
    Date of Patent: December 14, 2021
    Assignee: IOCURRENTS, INC.
    Inventors: Bhaskar Bhattacharyya, Cosmo King, Samuel Friedman, Kiersten Henderson, Alexa Rust
  • PREDICTION, PLANNING, AND OPTIMIZATION OF TRIP TIME, TRIP COST, AND/OR POLLUTANT EMISSION FOR A VEHICLE USING MACHINE LEARNING
    Publication number: 20200401743
    Abstract: A method of predicting, in real-time, a relationship between a vehicle's engine speed, trip time, cost, and fuel consumption, comprising: monitoring vehicle operation over time to acquiring data representing at least a vehicle location, a fuel consumption rate, and operating conditions; generating a predictive model relating the vehicle's engine speed, trip time, and fuel consumption; and receiving at least one constraint on the vehicle's engine speed, trip time, and fuel consumption, and automatically producing from at least one automated processor, based on the predictive model, a constrained output.
    Type: Application
    Filed: September 3, 2020
    Publication date: December 24, 2020
    Inventors: Bhaskar Bhattacharyya, Cosmo King, Samuel Friedman, Kiersten Henderson, Alexa Rust
  • Prediction, planning, and optimization of trip time, trip cost, and/or pollutant emission for a vehicle using machine learning
    Patent number: 10803213
    Abstract: A method of predicting, in real-time, a relationship between a vehicle's engine speed, trip time, cost, and fuel consumption, comprising: monitoring vehicle operation over time to acquiring data representing at least a vehicle location, a fuel consumption rate, and operating conditions; generating a predictive model relating the vehicle's engine speed, trip time, and fuel consumption; and receiving at least one constraint on the vehicle's engine speed, trip time, and fuel consumption, and automatically producing from at least one automated processor, based on the predictive model, a constrained output.
    Type: Grant
    Filed: November 8, 2019
    Date of Patent: October 13, 2020
    Assignee: IOCURRENTS, INC.
    Inventors: Bhaskar Bhattacharyya, Cosmo King, Samuel Friedman, Kiersten Henderson, Alexa Rust
  • NEAR REAL-TIME DETECTION AND CLASSIFICATION OF MACHINE ANOMALIES USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
    Publication number: 20200285997
    Abstract: A method of determining anomalous operation of a system includes: capturing a stream of data representing sensed (or determined) operating parameters of the system over a range of operating states, with a stability indicator representing whether the system was operating in a stable state when the operating parameters were sensed; determining statistical properties of the stream of data, including an amplitude-dependent parameter and a variance thereof over time parameter for an operating regime representing stable operation; determining a statistical norm for the statistical properties that distinguish between normal operation and anomalous operation of the system; responsive to detecting that normal and anomalous operation of the system can no longer be reliably distinguished, determining new statistical properties to distinguish between normal and anomalous system operation; and outputting a signal based on whether a concurrent stream of data representing sensed operating parameters of the system represent
    Type: Application
    Filed: March 3, 2020
    Publication date: September 10, 2020
    Inventors: Bhaskar Bhattacharyya, Samuel Friedman, Cosmo King, Kiersten Henderson
  • MACHINE LEARNING-BASED PREDICTION, PLANNING, AND OPTIMIZATION OF TRIP TIME, TRIP COST, AND/OR POLLUTANT EMISSION DURING NAVIGATION
    Publication number: 20200151291
    Abstract: A method of predicting, in real-time, a relationship between a vehicle's engine speed, trip time, cost, and fuel consumption, comprising: monitoring vehicle operation over time to acquiring data representing at least a vehicle location, a fuel consumption rate, and operating conditions; generating a predictive model relating the vehicle's engine speed, trip time, and fuel consumption; and receiving at least one constraint on the vehicle's engine speed, trip time, and fuel consumption, and automatically producing from at least one automated processor, based on the predictive model, a constrained output.
    Type: Application
    Filed: November 8, 2019
    Publication date: May 14, 2020
    Inventors: Bhaskar Bhattacharyya, Cosmo King, Samuel Friedman, Kiersten Henderson, Alexa Rust