Patents by Inventor Zhiping TANG

Zhiping TANG 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: 11900386
    Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.
    Type: Grant
    Filed: March 2, 2023
    Date of Patent: February 13, 2024
    Assignee: Walmart Apollo, LLC
    Inventors: Linhong Kang, Arthi Vijayakumar, YiYi Zeng, Zhiping Tang, Uday Akella, Mayra Rocio Harmon, Charlie Berry, Nathan Olds, John Fields, Kristin Danielle Piper
  • Publication number: 20230206243
    Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.
    Type: Application
    Filed: March 2, 2023
    Publication date: June 29, 2023
    Inventors: Linhong KANG, Arthi VIJAYAKUMAR, YiYi ZENG, Zhiping TANG, Uday AKELLA, Mayra Rocio HARMON, Charlie BERRY, Nathan OLDS, John FIELDS, Kristin Danielle PIPER
  • Publication number: 20230196133
    Abstract: Systems and methods for generating and using trained logit models using weight-of-evidence (WOE) based processes are disclosed. A training data set including sets of variables and a classification of each set in the sets of variables is received. The classification includes one of at least two potential classifications. A WOE feature set is generated by applying a binning process to each variable in the set of variables to generate one or more WOE transformations and applying the one or more WOE transformations to a corresponding variable in the set of variables. A trained logistic regression model is generated by applying an iterative training process based on the WOE feature set. The trained logistic regression model is configured to classify each set of variables. Production data sets are classified into one of the at least two potential classifications using the WOE transformations and the trained logistic regression model.
    Type: Application
    Filed: December 17, 2021
    Publication date: June 22, 2023
    Inventors: Tzu-Yen Wang, Jingru Zhou, Hui-Min Chen, James ZhiPing Tang
  • Patent number: 11631082
    Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.
    Type: Grant
    Filed: September 20, 2019
    Date of Patent: April 18, 2023
    Assignee: Walmart Apollo, LLC
    Inventors: Linhong Kang, Arthi Vijayakumar, YiYi Zeng, Zhiping Tang, Uday Akella, Mayra Rocio Harmon, Charlie Berry, Nathan Olds, John Fields, Kristin Danielle Piper
  • Publication number: 20220237445
    Abstract: Systems and methods for identifying anomalous interactions are disclosed. Interaction data representative of an interaction is received and the interaction is classified as one of an anomalous interaction or a benign interaction using an anomaly detection model. The anomaly detection model is configured to identify a similarity between the interaction data and known benign interactions. An indication of authorization is generated based on the classification of the interaction. The indication of authorization authorizes the interaction when the anomaly detection model classifies the interaction as benign and the indication of authorization denies the interaction when the anomaly detection model classifies the interaction as anomalous.
    Type: Application
    Filed: January 27, 2021
    Publication date: July 28, 2022
    Inventors: Tzu-Yen Wang, Jingru Zhou, Hui-Min Chen, Zhiping Tang
  • Patent number: 11250444
    Abstract: A method and system for identifying and labeling fraudulent store return activities includes receiving, by a server, retailer events from an online transaction system of a retailer, the retailer events comprising records of transactions between customers and the retailer, including sale, exchange and return activities across multiple stores. The retailer events are processed to build a network that associates stores, transactions, payment instruments, and customer identification over related activity sequences of transactions.
    Type: Grant
    Filed: November 1, 2017
    Date of Patent: February 15, 2022
    Assignee: Walmart Apollo, LLC
    Inventors: Yitao Yao, Sangita Fatnani, Guoyu Zhu, Pei Wang, Uday Akella, Jaya Kolhatkar, Vivek Crasta, Hui-Min Chen, Vidhya Raman, Zhiping Tang
  • Publication number: 20210090085
    Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.
    Type: Application
    Filed: September 20, 2019
    Publication date: March 25, 2021
    Inventors: Linhong KANG, Arthi VIJAYAKUMAR, YiYi ZENG, Zhiping TANG, Uday AKELLA, Mayra Rocio HARMON, Charlie BERRY, Nathan OLDS, John FIELDS, Kristin Danielle PIPER