Patents by Inventor Lee Feldkamp

Lee Feldkamp 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).

  • Patent number: 7353088
    Abstract: A system for detecting the presence of a human in a vehicle is provided. The system includes a vibration sensor that is configured to detect vibrations of the vehicle, and to output signals related to the sensed vibrations. A processor is configured to receive the signals output from the vibration sensor. The processor also operates a neural network that has a plurality of nodes, at least some of which are recurrent. The use of the recurrent nodes allows the output of a recurrent node to be fed back into itself, or another node. In addition, the output that is fed back can be combined with other inputs entering the node. In this way, the neural network can quickly learn to distinguish between various conditions, including an occupied state and an unoccupied state of the vehicle. The neural network provides an output indicating whether the vehicle is occupied.
    Type: Grant
    Filed: October 25, 2004
    Date of Patent: April 1, 2008
    Assignee: Ford Global Technologies, LLC
    Inventors: Charles Eagen, Lee Feldkamp, Sam Ebenstein, Kwaku Prakah-Asante, Yelena Rodin, Greg Smith
  • Publication number: 20060089753
    Abstract: A system for detecting the presence of a human in a vehicle is provided. The system includes a vibration sensor that is configured to detect vibrations of the vehicle, and to output signals related to the sensed vibrations. A processor is configured to receive the signals output from the vibration sensor. The processor also operates a neural network that has a plurality of nodes, at least some of which are recurrent. The use of the recurrent nodes allows the output of a recurrent node to be fed back into itself, or another node. In addition, the output that is fed back can be combined with other inputs entering the node. In this way, the neural network can quickly learn to distinguish between various conditions, including an occupied state and an unoccupied state of the vehicle. The neural network provides an output indicating whether the vehicle is occupied.
    Type: Application
    Filed: October 25, 2004
    Publication date: April 27, 2006
    Applicant: FORD GLOBAL TECHNOLOGIES, LLC
    Inventors: Charles Eagen, Lee Feldkamp, Sam Ebenstein, Kwaku Prakah-Asante, Yelena Rodin, Greg Smith