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    Graphical structure model-based transaction risk control
    3.
    发明授权
    Graphical structure model-based transaction risk control 有权

    公开(公告)号：US11526766B2

    公开(公告)日：2022-12-13

    申请号：US16805387

    申请日：2020-02-28

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Le Song ,  Hui Li ,  Zhibang Ge ,  Xin Huang ,  Chunyang Wen ,  Lin Wang ,  Tao Jiang ,  Yiguang Wang ,  Xiaofu Chang ,  Guanyin Zhu

    IPC: G06N3/08 ,  G06F16/23 ,  G06N3/04 ,  G06Q20/40 ,  G06Q40/02

    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.

    Processing sequential interaction data
    4.
    发明授权
    Processing sequential interaction data 有权

    公开(公告)号：US10936950B1

    公开(公告)日：2021-03-02

    申请号：US16816719

    申请日：2020-03-12

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Xiaofu Chang ,  Jianfeng Wen ,  Le Song

    IPC: G06N3/08 ,  G06F16/21 ,  G06N3/04

    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.

    Processing sequential interaction data
    5.
    发明授权
    Processing sequential interaction data 有权

    公开(公告)号：US11636341B2

    公开(公告)日：2023-04-25

    申请号：US17188112

    申请日：2021-03-01

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Xiaofu Chang ,  Jianfeng Wen ,  Le Song

    IPC: G06N3/08 ,  G06F16/21 ,  G06N3/04

    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.

    Method and apparatus for processing user interaction sequence data
    7.
    发明授权
    Method and apparatus for processing user interaction sequence data 有权

    公开(公告)号：US11250088B2

    公开(公告)日：2022-02-15

    申请号：US17222968

    申请日：2021-04-05

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Xiaofu Chang ,  Jianfeng Wen ,  Xuqin Liu ,  Le Song ,  Yuan Qi

    IPC: G06F16/90 ,  G06F16/9536 ,  G06F16/901

    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.

    Graphical structure model-based credit risk control
    9.
    发明授权
    Graphical structure model-based credit risk control 有权

    公开(公告)号：US11526936B2

    公开(公告)日：2022-12-13

    申请号：US16805538

    申请日：2020-02-28

    Applicant: Advanced New Technologies Co., Ltd.

    Inventor： Le Song ,  Hui Li ,  Zhibang Ge ,  Xin Huang ,  Chunyang Wen ,  Lin Wang ,  Tao Jiang ,  Yiguang Wang ,  Xiaofu Chang ,  Guanyin Zhu

    IPC: G06Q40/02 ,  G06N20/10 ,  G06K9/62 ,  G06N7/00

    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.

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