Patents by Inventor Panpan Qi

Panpan Qi 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).

  • Publication number: 20250232014
    Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.
    Type: Application
    Filed: December 24, 2024
    Publication date: July 17, 2025
    Inventors: Zhe Chen, Jiyi Zhang, Hewen Wang, Panpan Qi, Quan Jin Ferdinand Tang, Solomon kok how Teo, Yuzhen Zhuo, Mandar Ganaba Gaonkar, Fei Pei, Omkumar Mahalingam
  • Publication number: 20250211604
    Abstract: There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.
    Type: Application
    Filed: December 10, 2024
    Publication date: June 26, 2025
    Inventors: Panpan Qi, Zhe Chen, Quan Jin Ferdinand Tang, Fei Pei, Omkumar Mahalingam, Mandar Ganaba Gaonkar, Ting Lin, Gaurav Vishwanath Rane
  • Publication number: 20250211603
    Abstract: There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.
    Type: Application
    Filed: December 26, 2023
    Publication date: June 26, 2025
    Inventors: Panpan Qi, Zhe Chen, Quan Jin Ferdinand Tang, Fei Pei, Omkumar Mahalingam, Mandar Ganaba Gaonkar, Ting Lin, Gaurav Vishwanath Rane
  • Publication number: 20250077658
    Abstract: Techniques are disclosed that relate to predicting whether a computer-based interaction is being performed by a computer bot. A computer system may receive information describing exhibited user-presence indicators of different types that are associated with the computer-based interaction, including user-presence indicators indicative of whether the computer-based interaction is being performed by a computer bot. The computer system performs a first embedding operation to create a unified embedding that unifies the exhibited user-presence indicators into a single embedding that is representative of an aggregation of the exhibited user-presence indicators. The computer system performs a second embedding operation to create a difference embedding that is representative of a set of differences between expected user-presence indicators for the computer-based interaction and the exhibited user-presence indicators.
    Type: Application
    Filed: August 31, 2023
    Publication date: March 6, 2025
    Inventors: Zhe Chen, Panpan Qi, Solomon Kok How Teo, Yuzhen Zhuo, Quan Jin Ferdinand Tang, Omkumar Mahalingam, Fei Pei, Mandar Ganaba Gaonkar
  • Patent number: 12216745
    Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.
    Type: Grant
    Filed: December 27, 2022
    Date of Patent: February 4, 2025
    Assignee: PAYPAL, INC.
    Inventors: Zhe Chen, Jiyi Zhang, Hewen Wang, Panpan Qi, Quan Jin Ferdinand Tang, Solomon kok how Teo, Yuzhen Zhuo, Mandar Ganaba Gaonkar, Fei Pei, Omkumar Mahalingam
  • Publication number: 20240320471
    Abstract: Techniques for predicting whether a submission includes a forged image. A computer system receives a submission from a user that includes an image and image metadata, such as an identifier for the user and a User-Agent string value. An image pixel embedding is generated from the image, and a profile embedding is generated from the image metadata. The image embedding is indicative of whether the image is similar to known image forgeries. The profile embedding is generated from a user activity embedding indicative of User-Agent values associated with the user identifier. The profile embedding is generated using a machine learning model that uses stored parameters to associate user activity, device information, and forgery groups. The profile embedding thus indicates whether the user is associated with known image forgeries. The image pixel embedding and profile embedding are then used by a neural network to output a forgery prediction.
    Type: Application
    Filed: March 23, 2023
    Publication date: September 26, 2024
    Inventors: Zhe Chen, Panpan Qi, Jiazheng Zhang, Jiyi Zhang, Quan Jin Ferdinand Tang
  • Publication number: 20240211559
    Abstract: Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.
    Type: Application
    Filed: December 27, 2022
    Publication date: June 27, 2024
    Inventors: Zhe Chen, Jiyi Zhang, Hewen Wang, Panpan Qi, Quan Jin Ferdinand Tang, Solomon kok how Teo, Yuzhen Zhuo, Mandar Ganaba Gaonkar, Fei Pei, Omkumar Mahalingam