Patents by Inventor Cece Wang

Cece Wang 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).

  • Cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning
    Patent number: 12632323
    Abstract: Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.
    Type: Grant
    Filed: September 6, 2023
    Date of Patent: May 19, 2026
    Assignee: China Jiliang University
    Inventors: Jiawei Lu, Gang Xiao, Qibing Wang, jiahong Zheng, Dongjin Yu, Xudong Zhang, Zhenbo Chen, Jun Xu, Duanni Li, Cece Wang
  • CLOUD-NATIVE APPLICATION PROGRAMMING INTERFACE (API) RECOMMENDATION METHOD FUSING DATA AUGMENTATION AND CONTRASTIVE LEARNING
    Publication number: 20250013514
    Abstract: Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.
    Type: Application
    Filed: September 6, 2023
    Publication date: January 9, 2025
    Applicant: China Jiliang University
    Inventors: Jiawei Lu, Gang Xiao, Qibing Wang, jiahong Zheng, Dongjin Yu, Xudong Zhang, Zhenbo Chen, Jun Xu, Duanni Li, Cece Wang