Patents by Inventor Yuanqiao WU

Yuanqiao WU 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).

  • K-LSTM architecture for purchase prediction
    Patent number: 12293261
    Abstract: A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction.
    Type: Grant
    Filed: June 13, 2019
    Date of Patent: May 6, 2025
    Assignee: ROYAL BANK OF CANADA
    Inventors: Yuanqiao Wu, Janahan Ramanan, Jaspreet Sahota, Cathal Smyth, Yik Chau Lui
  • Correcting bias in supervised machine learning data
    Patent number: 11568308
    Abstract: An electronic device and method of correcting bias for supervised machine learning data is provided. The electronic device comprises a processor and memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises training an auto-encoder with an unbiased subset of historical data, and applying the auto-encoder to correct historical data.
    Type: Grant
    Filed: June 13, 2019
    Date of Patent: January 31, 2023
    Assignee: ROYAL BANK OF CANADA
    Inventors: Jaspreet Sahota, Janahan Ramanan, Yuanqiao Wu, Yik Chau Lui
  • CORRECTING BIAS IN SUPERVISED MACHINE LEARNING DATA
    Publication number: 20190385079
    Abstract: An electronic device and method of correcting bias for supervised machine learning data is provided. The electronic device comprises a processor and memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises training an auto-encoder with an unbiased subset of historical data, and applying the auto-encoder to correct historical data.
    Type: Application
    Filed: June 13, 2019
    Publication date: December 19, 2019
    Inventors: Jaspreet SAHOTA, Janahan RAMANAN, Yuanqiao WU, Yik Chau LUI
  • K-LSTM ARCHITECTURE FOR PURCHASE PREDICTION
    Publication number: 20190385080
    Abstract: A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction.
    Type: Application
    Filed: June 13, 2019
    Publication date: December 19, 2019
    Inventors: Yuanqiao WU, Janahan RAMANAN, Jaspreet SAHOTA, Cathal SMYTH, Yik Chau LUI