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公开(公告)号:US12292905B2
公开(公告)日:2025-05-06
申请号:US18095196
申请日:2023-01-10
Inventor: Haifeng Sun , Zirui Zhuang , Bing Ma , Jingyu Wang , Cheng Zhang , Tong Xu , Jing Wang
IPC: G06F17/00 , G06F16/3329 , G06F16/334 , G06N3/08
Abstract: The multi-turn dialogue system based on retrieval includes the following modules: a representation module, a matching module, an aggregation module and a prediction module; the multi-turn dialogue method based on retrieval includes the following steps: (1) by a representation module, converting each turn of dialogue into a cascade vector of the dialogue, and converting a candidate answer into a cascade vector of the candidate answer; (2) by a matching module, dynamically absorbing context information based on a global attention mechanism, and calculating a matching vector; (3) by aggregation module, obtaining a short-term dependence information sequence and a long-term dependence information sequence; (4) by a prediction module, calculating the matching score of the context and candidate answer involved in the matching; (5) selecting a candidate answer with the highest matching score as a correct answer.
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公开(公告)号:US11909592B2
公开(公告)日:2024-02-20
申请号:US18115453
申请日:2023-02-28
Inventor: Jingyu Wang , Qi Qi , Zirui Zhuang , Yuebin Guo , Lingqi Guo , Tong Xu , Jing Wang
IPC: H04L41/0894 , H04L41/08
CPC classification number: H04L41/0894 , H04L41/0886
Abstract: A method for multi-policy conflict avoidance in an autonomous network comprising: collecting network state information; acquiring a set of multiple policies to be verified; constructing a policy ordering space tree containing all multi-policy execution sequences; performing a depth-first traversal on the policy ordering space tree, extracting a multi-policy execution sequence to be verified, then constructing an initial simulation data plane, injecting each policy in the multi-policy execution sequence into the simulation data plane one by one in sequence, and storing a simulation data plane after each policy is inserted; detecting whether there is a conflict in the simulation data plane generated after each policy is executed, inferring dependencies among multiple policies in the conflict policy sequence, pruning the policy ordering space tree, to efficiently select and update the multi-policy execution sequence avoiding conflicts.
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公开(公告)号:US11974027B2
公开(公告)日:2024-04-30
申请号:US18115110
申请日:2023-02-28
Inventor: Zirui Zhuang , Jingyu Wang , Huairuo Xu , Haifeng Sun , Daoxu Sheng , Jing Wang
IPC: H04N21/81 , H04N21/24 , H04N21/414 , H04N21/6405 , H04N21/647
CPC classification number: H04N21/816 , H04N21/2407 , H04N21/41407 , H04N21/6405 , H04N21/64769
Abstract: A system and method for real-time transmission of a panoramic video, which propose a new grouping method and bitrate decision method specifically for a panoramic 360-degree video. The grouping method takes into account fields of view of different users, thus effectively reducing the bandwidth consumption of repeated video segments, while ensuring user fairness and making full use of network bandwidth resources when allocating resources. The system and method of the invention can maximize the long-term QoE of users under the condition of limited bandwidth, and avoid the problem that the existing schemes are complex and difficult to meet real-time requirements of users.
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公开(公告)号:US20230401243A1
公开(公告)日:2023-12-14
申请号:US18095196
申请日:2023-01-10
Inventor: Haifeng Sun , Zirui Zhuang , Bing Ma , Jingyu Wang , Cheng Zhang , Tong Xu , Jing Wang
IPC: G06F16/332 , G06F16/33 , G06N3/08
CPC classification number: G06F16/3329 , G06F16/3347 , G06F16/3344 , G06N3/08
Abstract: The multi-turn dialogue system based on retrieval includes the following modules: a representation module, a matching module, an aggregation module and a prediction module; the multi-turn dialogue method based on retrieval includes the following steps: (1) by a representation module, converting each turn of dialogue into a cascade vector of the dialogue, and converting a candidate answer into a cascade vector of the candidate answer; (2) by a matching module, dynamically absorbing context information based on a global attention mechanism, and calculating a matching vector; (3) by aggregation module, obtaining a short-term dependence information sequence and a long-term dependence information sequence; (4) by a prediction module, calculating the matching score of the context and candidate answer involved in the matching; (5) selecting a candidate answer with the highest matching score as a correct answer.
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