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公开(公告)号：US11947552B2
公开(公告)日：2024-04-02
申请号：US17947659
申请日：2022-09-19
Inventor： Xu Li , Yunfeng Cai , Mingming Sun , Ping Li
IPC: G06F17/00 , G06F16/22 , G06F16/2458
CPC classification number: G06F16/2465 , G06F16/2237
Abstract: A method for discovering causality from data includes acquiring to-be-processed data, and obtaining a covariance matrix of the to-be-processed data; determining a first target column in the covariance matrix, taking the number of columns of the first target column as a first place in a rearrangement sequence, and obtaining a first upper triangular matrix according to the first target column; determining a position of the number of columns of the covariance matrix other than the first target column except the first place in the rearrangement sequence according to the first target column and the first upper triangular matrix, and obtaining an upper triangular matrix in each position determination; obtaining an adjacency matrix according to an upper triangular matrix and a rearrangement sequence obtained in final position determination; and generating directed acyclic graph (DAG) by using the adjacency matrix, and taking the DAG as causality discovery result of the to-be-processed data.
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公开(公告)号：US20240104403A1
公开(公告)日：2024-03-28
申请号：US18521061
申请日：2023-11-28
Inventor： Yaqing Wang , Hongming Piao , Longteng Xu , Daxiang Dong , Jingbo Zhou
IPC: G06N5/022
CPC classification number: G06N5/022
Abstract: A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.
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公开(公告)号：US20240104154A1
公开(公告)日：2024-03-28
申请号：US18016754
申请日：2022-07-25
Inventor： Zhou CHENG
IPC: G06F16/9538 , G06N3/0455
CPC classification number: G06F16/9538 , G06N3/0455
Abstract: A method is provided that includes: determining a plurality of recall data associated with data to be searched; determining, for each recall data of the plurality of recall data, a recommendation degree of the recall data based on a similarity between the recall data and each recall data of the plurality of recall data; and ranking the recall data in the plurality of recall data based on the recommendation degree of each recall data of the plurality of recall data.
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公开(公告)号：US11929097B2
公开(公告)日：2024-03-12
申请号：US17507313
申请日：2021-10-21
Inventor： Yanlong Chang
CPC classification number: G11B27/02 , G10K15/02 , G11B20/00007 , G11B27/19 , G11B2020/00014 , G11B2020/00072
Abstract: The present disclosure provides a method and apparatus of editing a multimedia file, an electronic device, and a storage medium. The method includes: acquiring a multimedia file and an operation information contained in each editing track of a plurality of editing tracks, wherein the operation information is acquired to edit the multimedia file; detecting a multimedia file frame contained in the each editing track at a current time in a case of editing the multimedia file in a chronological order; editing the multimedia file frame contained in the each editing track at the current time by using the operation information associated with the multimedia file frame, so as to obtain an edited file frame contained in the each editing track at the current time; and performing an encoding synthesis on the edited file frame contained in the each editing track at the current time.
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公开(公告)号：US20240070500A1
公开(公告)日：2024-02-29
申请号：US17899575
申请日：2022-08-30
Inventor： Kun Fang
IPC: G06N10/20
CPC classification number: G06N10/20
Abstract: A method for operating a quantum computer is provided. The method includes: obtaining a quantum gate parameter of each quantum gate in a quantum circuit to be simulated; and generating a sub-measurement mode equivalent to the quantum gate based on the quantum gate parameter. The method further includes combining the sub-measurement mode equivalent to each quantum gate to obtain an overall measurement mode equivalent to the quantum circuit to be simulated as a whole; and sorting an operation order of the operation commands of each sub-measurement mode in the overall measurement mode, thereby obtaining the sorted overall measurement mode as a simulation result. This solution transforms the quantum circuit used for quantum computation into an equivalent measurement mode according to a one-way quantum computer computation model. Direct simulation computation on the quantum circuit can be avoided, and accordingly a simulation operation amount of a classical computer is greatly reduced.
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Patent Agency Ranking
86.发明公开公开(公告)号：US20240070454A1
公开(公告)日：2024-02-29
申请号：US18108956
申请日：2023-02-13
Inventor： Ruoyu GUO , Yuning DU , Chenxia LI , Baohua LAI , Yanjun MA
Abstract: Provided is a lightweight model training method, an image processing method, a device and a medium. The lightweight model training method includes: acquiring first and second augmentation probabilities and a target weight adopted in an e-th iteration; performing data augmentation on a data set based on the first and second augmentation probabilities respectively, to obtain first and second data sets; obtaining a first output value of a student model and a second output value of a teacher model based on the first data set; obtaining a third output value and a fourth output value based on the second data set; determining a distillation loss function, a truth-value loss function and a target loss function; training the student model based on the target loss function; and determining a first augmentation probability or target weight to be adopted in an (e+1)-th iteration in a case of e is less than E.
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公开(公告)号：US20240062654A1
公开(公告)日：2024-02-22
申请号：US17753817
申请日：2021-11-17
Inventor： Jizhou Huang , Deguo Xia , Haifeng Wang
IPC: G08G1/0968 , G08G1/01 , G08G1/0967 , G08G1/052
CPC classification number: G08G1/0968 , G08G1/0145 , G08G1/0112 , G08G1/096725 , G08G1/052
Abstract: The present disclosure discloses a vehicle control method and apparatus, a device and a computer storage medium, and relates to the technical fields of autonomous driving and intelligent transportation. A specific implementation solution involves: determining vehicles in a preset geo-fencing region; determining a vehicle weight of each said vehicles according to a vehicle type and a waiting duration of each said vehicles; estimating, according to the vehicle weights of the vehicles in each of lanes in the geo-fencing region and positions of the vehicles in each said lanes, a duration to be waited in each said lanes; and generating a control instruction for each said vehicles according to the respective durations to be waited in each said lanes and the respective positions of the vehicles in each said lanes, the control instruction including a state instruction and/or a target speed instruction. According to the present disclosure, global scheduling decisions can be performed on vehicles in a geo-fencing region, so as to alleviate traffic congestion.
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公开(公告)号：US20240062351A1
公开(公告)日：2024-02-22
申请号：US18113860
申请日：2023-02-24
Inventor： Wei WANG , Yizhuo LIU
CPC classification number: G06T7/0002 , G06V20/49 , H04N9/64 , G06T7/90 , G06T2207/10024 , G06T2207/10016 , G06T2207/20021 , G06T2207/30168
Abstract: Provided is a method for evaluating video quality, an electronic device and a storage medium, relating to the field of computer technology, and in particular, to the field of artificial intelligence, cloud computing, computer vision and deep learning technologies. The method includes: determining a plurality of video frames containing a target object in a target video; obtaining HSL data of an image area corresponding to the target object in the plurality of video frames; and evaluating a quality of the target video according to the HSL data.
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公开(公告)号：US11907668B2
公开(公告)日：2024-02-20
申请号：US18148904
申请日：2022-12-30
Inventor： Minlong Peng , Mingming Sun , Ping Li
Abstract: The present disclosure provides a method for selecting an annotated sample. The method includes: determining a first attribute and a second attribute of a sample characteristic; in which the first attribute is a characteristic attribute of the sample characteristic in a source field sample set, and the second attribute is a characteristic attribute of the sample characteristic in a target field sample set; and determining a target annotated sample from a plurality of candidate annotated samples of the source field sample set according to the first attribute and the second attribute; in which the target annotated sample is configured to train a classification model, the classification model includes a model for determining an emotion polarity by analyzing an input sample to be classified.
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公开(公告)号：US11900918B2
公开(公告)日：2024-02-13
申请号：US17451380
申请日：2021-10-19
Inventor： Liao Zhang , Zhengxiang Jiang , Xiaoyin Fu
IPC: G10L15/06 , G06F40/253 , G06F40/30
CPC classification number: G10L15/063 , G06F40/253 , G06F40/30
Abstract: The present disclosure provides a method for training a linguistic model, related to fields of speech, natural language processing, deep learning technologies. A method includes: obtaining grammars corresponding to a plurality of sample texts and a slot value of a slot in each grammar by using semantic analysis; generating a grammar graph corresponding to each grammar based on the corresponding grammar and the slot value of the slot in the corresponding grammar; obtaining a weight of each grammar, a weight of each slot, and a weight of each slot value in each grammar graph based on the sample texts; determining at least one grammar frequency of each order based on the weight of each grammar, the weight of each slot, and the weight of each slot value in each grammar graph; and training the linguistic model based on the at least one grammar frequency of each order.
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