Patents by Inventor Xiaofu Chang

Xiaofu Chang 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: 11102230
    Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.
    Type: Grant
    Filed: March 4, 2020
    Date of Patent: August 24, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventors: Le Song, Hui Li, Zhibang Ge, Xin Huang, Chunyang Wen, Lin Wang, Tao Jiang, Yiguang Wang, Xiaofu Chang, Guanyin Zhu
  • Publication number: 20210234881
    Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.
    Type: Application
    Filed: April 15, 2021
    Publication date: July 29, 2021
    Applicant: Advanced New Technologies Co., Ltd.
    Inventors: Le Song, Hui Li, Zhibang Ge, Xin Huang, Chunyang Wen, Lin Wang, Tao Jiang, Yiguang Wang, Xiaofu Chang, Guanyin Zhu
  • Publication number: 20210224347
    Abstract: Computer-implemented methods, computer-implemented systems, and non-transitory, computer-readable media for processing interaction sequence data are disclosed. One computer-implemented method includes: obtaining a dynamic interaction graph is obtained, where the dynamic interaction graph is constructed based on a dynamic interaction sequence, including a plurality of interactions arranged in a chronological order, where each interaction includes two objects involved in the interaction and a time of the interaction. In the dynamic interaction graph, a sub-graph corresponding to a target node is determined, where nodes in the sub-graph comprise the target node and connection nodes connected to the target node through a predetermined amount of edges originating from the target node. A feature vector corresponding to the target node is determined based on a node feature of each of the nodes of the sub-graph and directions of edges of the sub-graph.
    Type: Application
    Filed: April 5, 2021
    Publication date: July 22, 2021
    Applicant: Advanced New Technologies Co., Ltd.
    Inventors: Xiaofu Chang, Jianfeng Wen, Xuqin Liu, Le Song, Yuan Qi
  • Publication number: 20210182680
    Abstract: This disclosure relates to processing sequential interaction data through machine learning. In one aspect, a method includes obtaining a dynamic interaction graph constructed based on a dynamic interaction sequence. The dynamic interaction sequence includes interaction feature groups corresponding to interaction events. Each interaction feature group includes a first object, a second object, and an interaction time of an interaction event that involved the first object and the second object. The dynamic interaction graph includes multiple nodes including, for each interaction feature group, a first node that represents the first object of the interaction feature group and a second node that represents the second object of the interaction feature group. A current sequence corresponding to a current node to be analyzed is determined. The current sequence is input into a Transformer-based neural network model. The neural network model determines a feature vector corresponding to the current node.
    Type: Application
    Filed: March 1, 2021
    Publication date: June 17, 2021
    Applicant: Advanced Technologies Co., LTd.
    Inventors: Xiaofu Chang, Jianfeng Wen, Le Song
  • Patent number: 10970350
    Abstract: Computer-implemented methods, computer-implemented systems, and non-transitory, computer-readable media for processing interaction sequence data are disclosed. One computer-implemented method includes: obtaining a dynamic interaction graph is obtained, where the dynamic interaction graph is constructed based on a dynamic interaction sequence, including a plurality of interactions arranged in a chronological order, where each interaction includes two objects involved in the interaction and a time of the interaction. In the dynamic interaction graph, a sub-graph corresponding to a target node is determined, where nodes in the sub-graph comprise the target node and connection nodes connected to the target node through a predetermined amount of edges originating from the target node. A feature vector corresponding to the target node is determined based on a node feature of each of the nodes of the sub-graph and directions of edges of the sub-graph.
    Type: Grant
    Filed: March 9, 2020
    Date of Patent: April 6, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventors: Xiaofu Chang, Jianfeng Wen, Xuqin Liu, Le Song, Yuan Qi
  • Patent number: 10936950
    Abstract: This disclosure relates to processing sequential interaction data through machine learning. In one aspect, a method includes obtaining a dynamic interaction graph constructed based on a dynamic interaction sequence. The dynamic interaction sequence includes interaction feature groups corresponding to interaction events. Each interaction feature group includes a first object, a second object, and an interaction time of an interaction event that involved the first object and the second object. The dynamic interaction graph includes multiple nodes including, for each interaction feature group, a first node that represents the first object of the interaction feature group and a second node that represents the second object of the interaction feature group. A current sequence corresponding to a current node to be analyzed is determined. The current sequence is input into a Transformer-based neural network model. The neural network model determines a feature vector corresponding to the current node.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: March 2, 2021
    Assignee: Advanced New Technologies Co., Ltd.
    Inventors: Xiaofu Chang, Jianfeng Wen, Le Song
  • Publication number: 20210049458
    Abstract: This disclosure relates to processing sequential interaction data through machine learning. In one aspect, a method includes obtaining a dynamic interaction graph constructed based on a dynamic interaction sequence. The dynamic interaction sequence includes interaction feature groups corresponding to interaction events. Each interaction feature group includes a first object, a second object, and an interaction time of an interaction event that involved the first object and the second object. The dynamic interaction graph includes multiple nodes including, for each interaction feature group, a first node that represents the first object of the interaction feature group and a second node that represents the second object of the interaction feature group. A current sequence corresponding to a current node to be analyzed is determined. The current sequence is input into a Transformer-based neural network model. The neural network model determines a feature vector corresponding to the current node.
    Type: Application
    Filed: March 12, 2020
    Publication date: February 18, 2021
    Applicant: Alibaba Group Holding Limited
    Inventors: Xiaofu Chang, Jianfeng Wen, Le Song
  • Publication number: 20210049225
    Abstract: Computer-implemented methods, computer-implemented systems, and non-transitory, computer-readable media for processing interaction sequence data are disclosed. One computer-implemented method includes: obtaining a dynamic interaction graph is obtained, where the dynamic interaction graph is constructed based on a dynamic interaction sequence, including a plurality of interactions arranged in a chronological order, where each interaction includes two objects involved in the interaction and a time of the interaction. In the dynamic interaction graph, a sub-graph corresponding to a target node is determined, where nodes in the sub-graph comprise the target node and connection nodes connected to the target node through a predetermined amount of edges originating from the target node. A feature vector corresponding to the target node is determined based on a node feature of each of the nodes of the sub-graph and directions of edges of the sub-graph.
    Type: Application
    Filed: March 9, 2020
    Publication date: February 18, 2021
    Inventors: Xiaofu Chang, Jianfeng Wen, Xuqin Liu, Le Song, Yuan Qi
  • Patent number: 10747959
    Abstract: A dialog generation method includes: training a sequence to sequence (seq2seq)-based dialog model using a loss function including topic range constraint information; and generating a dialog using the trained dialog model. With the dialog generation method, topic range constraint information is introduced in the process of dialog model training using a loss function including the topic range constraint information, thus helping to prevent the trained model from producing low-quality meaningless replies.
    Type: Grant
    Filed: January 31, 2020
    Date of Patent: August 18, 2020
    Assignee: Alibaba Group Holding Limited
    Inventors: Xiaofu Chang, Linlin Chao, Peng Xu, Xiaolong Li
  • Publication number: 20200204577
    Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.
    Type: Application
    Filed: March 4, 2020
    Publication date: June 25, 2020
    Applicant: Alibaba Group Holding Limited
    Inventors: Le Song, Hui Li, Zhibang Ge, Xin Huang, Chunyang Wen, Lin Wang, Tao Jiang, Yiguang Wang, Xiaofu Chang, Guanyin Zhu
  • Publication number: 20200202219
    Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.
    Type: Application
    Filed: February 28, 2020
    Publication date: June 25, 2020
    Applicant: Alibaba Group Holding Limited
    Inventors: Le Song, Hui Li, Zhibang Ge, Xin Huang, Chunyang Wen, Lin Wang, Tao Jiang, Yiguang Wang, Xiaofu Chang, Guanyin Zhu
  • Publication number: 20200202428
    Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.
    Type: Application
    Filed: February 28, 2020
    Publication date: June 25, 2020
    Applicant: Alibaba Group Holding Limited
    Inventors: Le Song, Hui Li, Zhibang Ge, Xin Huang, Chunyang Wen, Lin Wang, Tao Jiang, Yiguang Wang, Xiaofu Chang, Guanyin Zhu
  • Publication number: 20200167388
    Abstract: Data storage and calling methods and devices are provided. One of the methods includes: receiving first motion data and business data; establishing an association relationship between the first motion data and the business data and storing the association relationship; receiving second motion data; and determining first motion data that matches the second motion data, and returning, to a sender of the second motion data, business data associated with the matched first motion data.
    Type: Application
    Filed: January 28, 2020
    Publication date: May 28, 2020
    Inventors: Kaisheng YAO, Peng Xu, Yuan Qi, Xiaofu Chang
  • Publication number: 20200167528
    Abstract: A dialog generation method includes: training a sequence to sequence (seq2seq)-based dialog model using a loss function including topic range constraint information; and generating a dialog using the trained dialog model. With the dialog generation method, topic range constraint information is introduced in the process of dialog model training using a loss function including the topic range constraint information, thus helping to prevent the trained model from producing low-quality meaningless replies.
    Type: Application
    Filed: January 31, 2020
    Publication date: May 28, 2020
    Inventors: Xiaofu CHANG, Linlin CHAO, Peng XU, Xiaolong LI
  • Publication number: 20200110916
    Abstract: A dialog generation method includes: training a sequence to sequence (seq2seq)-based dialog model using a loss function including topic range constraint information; and generating a dialog using the trained dialog model. With the dialog generation method, topic range constraint information is introduced in the process of dialog model training using a loss function including the topic range constraint information, thus helping to prevent the trained model from producing low-quality meaningless replies.
    Type: Application
    Filed: December 5, 2019
    Publication date: April 9, 2020
    Inventors: Xiaofu CHANG, Linlin Chao, Peng Xu, Xiaolong Li
  • Publication number: 20190294632
    Abstract: Data storage and calling methods and devices are provided. One of the methods includes: receiving first motion data and business data; establishing an association relationship between the first motion data and the business data and storing the association relationship; receiving second motion data; and determining first motion data that matches the second motion data, and returning, to a sender of the second motion data, business data associated with the matched first motion data.
    Type: Application
    Filed: June 12, 2019
    Publication date: September 26, 2019
    Inventors: Kaisheng YAO, Peng Xu, Yuan Qi, Xiaofu Chang