Resource scheduling method and apparatus, and storage medium

    公开(公告)号:US11573836B2

    公开(公告)日:2023-02-07

    申请号:US17142770

    申请日:2021-01-06

    Abstract: A resource scheduling method and apparatus, an electronic device, and a storage medium are provided, which are related to the technical field of system resource scheduling. The resource scheduling method comprises: monitoring whether a current system can bear a load of a target application which has triggered and entered a high-computational-power scenario, subjecting the system to resource scheduling if the system is monitored to be unable to bear the load of the target application, and running the target application in the high-computational-power scenario based on scheduled system resources.

    Method and apparatus for processing risk-management feature factors, electronic device and storage medium

    公开(公告)号:US11568347B2

    公开(公告)日:2023-01-31

    申请号:US17126826

    申请日:2020-12-18

    Abstract: A method and apparatus for processing risk-management feature factors based on user generated content (UGC), an electronic device and a storage medium are disclosed, which relates to the fields of artificial intelligence and cloud computing. An implementation includes generating a feature expression of the UGC based on the UGC; and extracting the risk-management feature factors of the UGC according to a pre-generated risk-management-feature-factor extracting model and the feature expression of the UGC. According to the technology of the present application, the risk-management feature factors of a corresponding user may be extracted based on the UGC without depending on privacy information of the user, such as personal basic attributes, or the like, such that subsequent related processing actions of risk management may be facilitated, an acquiring way and an acquiring mode of the risk-management feature factors may be enriched effectively, and richer information of the risk-management feature factors may be acquired.

    Interactive method and device of robot, and device

    公开(公告)号:US11551673B2

    公开(公告)日:2023-01-10

    申请号:US16396142

    申请日:2019-04-26

    Inventor: Jun Dai Ying Liu

    Abstract: Embodiments of the present disclosure provide an interactive method of a robot, an interactive device of a robot and a device. The method includes: obtaining voice information input by an interactive object, and performing semantic recognition on the voice information to obtain a conversation intention; obtaining feedback information corresponding to the conversation intention based on a conversation scenario knowledge base pre-configured by a simulated user; and converting the feedback information into a voice of the simulated user, and playing the voice to the interactive object.

    METHOD, APPARATUS, ELECTRONIC DEVICE AND STORAGE MEDIUM FOR TRAINING SEMANTIC SIMILARITY MODEL

    公开(公告)号:US20230004753A9

    公开(公告)日:2023-01-05

    申请号:US17209051

    申请日:2021-03-22

    Inventor: Zhen Li Yukun Li Yu Sun

    Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.

    Method for translating image and method for training image translation model

    公开(公告)号:US11526971B2

    公开(公告)日:2022-12-13

    申请号:US17107196

    申请日:2020-11-30

    Abstract: The present disclosure provides a computer-implemented method for translating an image and a computer-implemented method for training an image translation model. In the computer-implemented method for translating an image, an image translation request carrying an original image is obtained. The original image is processed to generate a pre-translated image, a mask image and a deformation parameter. The original image is deformed based on the deformation parameter to obtain a deformed image. The deformed image, the pre-translated image and the mask image are merged to generate a target translated image.

    Method and apparatus for error correction of numerical contents in text, and storage medium

    公开(公告)号:US11526657B2

    公开(公告)日:2022-12-13

    申请号:US17375225

    申请日:2021-07-14

    Abstract: This application discloses a method, an apparatus and an electronic device for error correction of numerical contents in a text, and relates to a technology field of artificial intelligence such as natural language processing and deep learning. The implementation method is: obtaining a target text to be processed; determining original numerical contents included in the target text; determining target types corresponding to the original numerical contents; and performing error correction on each original numerical content according to an error correction manner corresponding to each target type. Therefore, the error correction of numerical contents is realized according to types of the numerical contents, which is not only limited to the error correction of the numerical format, but also the logical error correction of the numerical content, so as to improve the numerical error correction capability and thereby improving the recall rate of detection and correction of wrong values.

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