Patents by Inventor Carissa Lew

Carissa Lew 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: 10860929
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: May 11, 2020
    Date of Patent: December 8, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Publication number: 20200272903
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: May 11, 2020
    Publication date: August 27, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10685282
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Grant
    Filed: November 7, 2018
    Date of Patent: June 16, 2020
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10594338
    Abstract: A compression system includes an encoder and a decoder. The encoder can be deployed by a sender system to encode a tensor for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode and reconstruct the encoded tensor. The encoder receives a tensor for compression. The encoder also receives a quantization mask and probability data associated with the tensor. Each element of the tensor is quantized using an alphabet size allocated to that element by the quantization mask data. The encoder compresses the tensor by entropy coding each element using the probability data and alphabet size associated with the element. The decoder receives the quantization mask data, the probability data, and the compressed tensor data. The quantization mask and probabilities are used to entropy decode and subsequently reconstruct the tensor.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: March 17, 2020
    Assignee: WaveOne Inc.
    Inventors: Carissa Lew, Steven Branson, Oren Rippel, Sanjay Nair, Alexander Grant Anderson, Lubomir Bourdev
  • Patent number: 10565499
    Abstract: An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: February 18, 2020
    Assignee: WaveOne Inc.
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel
  • Publication number: 20200036995
    Abstract: An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.
    Type: Application
    Filed: November 7, 2018
    Publication date: January 30, 2020
    Inventors: Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander Anderson, Lubomir Bourdev
  • Patent number: 10402722
    Abstract: A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.
    Type: Grant
    Filed: December 15, 2017
    Date of Patent: September 3, 2019
    Assignee: WaveOne Inc.
    Inventors: Oren Rippel, Lubomir Bourdev, Carissa Lew, Sanjay Nair
  • Publication number: 20180176578
    Abstract: A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Oren Rippel, Lubomir Bourdev, Carissa Lew, Sanjay Nair
  • Publication number: 20180174052
    Abstract: The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Oren Rippel, Lubomir Bourdev, Carissa Lew, Sanjay Nair
  • Publication number: 20180174047
    Abstract: A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel
  • Publication number: 20180174275
    Abstract: An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.
    Type: Application
    Filed: December 15, 2017
    Publication date: June 21, 2018
    Inventors: Lubomir Bourdev, Carissa Lew, Sanjay Nair, Oren Rippel