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    DATA PROCESSING METHOD
    11.
    发明公开
    DATA PROCESSING METHOD 审中-公开

    公开(公告)号：US20240330328A1

    公开(公告)日：2024-10-03

    申请号：US18741744

    申请日：2024-06-12

    Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    Inventor： Siyuan HAO ,  Le ZHANG ,  Le DAI ,  Jingbo ZHOU ,  Shengming ZHANG ,  Chuan QIN ,  Hui XIONG

    IPC: G06F16/28

    CPC classification number: G06F16/288

    Abstract: A method is provided. The method includes: obtaining an object relationship diagram; for a target object of a plurality of first objects, obtaining at least one meta-path corresponding to the target object in the object relationship diagram; for each meta-path, performing the following operations: determining a plurality of first attention weights of the target object based on inherent attribute data of the target object and inherent attribute data of each of a plurality of second objects on the meta-path; obtaining a second representation vector of the target object based on a first representation vector of the target object and the plurality of first attention weights; and obtaining a target indicator prediction result of the target object based at least on at least one second representation vector of the target object corresponding to the at least one meta-path.

    METHOD OF IMPORTING DATA TO DATABASE, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    12.
    发明公开
    METHOD OF IMPORTING DATA TO DATABASE, ELECTRONIC DEVICE, AND STORAGE MEDIUM 审中-公开

    公开(公告)号：US20240273113A1

    公开(公告)日：2024-08-15

    申请号：US18642571

    申请日：2024-04-22

    Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

    Inventor： Ming ZHANG ,  Yanyan LI ,  Lili LI ,  Jingbo ZHOU

    IPC: G06F16/25

    CPC classification number: G06F16/258

    Abstract: The present application relates to a field of big data technology, in particular to a field of data storage technology. More specifically, the present disclosure relates to a method of importing data to a database, an electronic device, and a storage medium. A specific implementation solution is: acquiring incoming data from a data source according to a database config file; the incoming data is original data directly acquired from the data source; calculating and processing the incoming data according to the database config file to obtain computational data; the computational data is obtained by integrating and calculating the incoming data; writing the incoming data and the computational data into a database.

    ONLINE RIDE-HAILING INFORMATION PROCESSING METHOD, DEVICE AND COMPUTER STORAGE MEDIUM
    13.
    发明公开
    ONLINE RIDE-HAILING INFORMATION PROCESSING METHOD, DEVICE AND COMPUTER STORAGE MEDIUM 审中-公开

    公开(公告)号：US20240169462A1

    公开(公告)日：2024-05-23

    申请号：US17758687

    申请日：2021-11-17

    Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    Inventor： Hao ZHANG ,  Jingbo ZHOU ,  Jizhou HUANG

    IPC: G06Q50/47 ,  G06Q30/0283 ,  G06Q30/0601

    CPC classification number: G06Q50/47 ,  G06Q30/0284 ,  G06Q30/0635

    Abstract: An online ride-hailing information processing method and apparatus, a device, and a computer storage medium, relating to big data computing and deep learning technologies in the field of AI technologies, are disclosed. A specific solution involves: acquiring an online ride-hailing query condition including information of an origin and a destination sent by a client; determining a query time range according to the query condition; calculating cost information of arrival at the destination departing at a plurality of times in the query time range respectively; determining, according to the cost information of arrival at the destination departing at the plurality of times, a time meeting the query condition as a recommended departure time; determining a recommended order-sending time according to the recommended departure time; and returning a query result to the client, the query result including the recommended order-sending time, or further including cost information corresponding to the recommended order-sending time. According to the present disclosure, users can select a low-cost order-sending time, which improves user experience, saves network resources, and reduces the influence on system performance.

    DATA QUERY METHOD AND APPARATUS BASED ON LARGE MODEL, ELECTRONIC DEVICE, AND STORAGE MEDIUM
    15.
    发明申请
    DATA QUERY METHOD AND APPARATUS BASED ON LARGE MODEL, ELECTRONIC DEVICE, AND STORAGE MEDIUM 有权

    公开(公告)号：US20250103589A1

    公开(公告)日：2025-03-27

    申请号：US18974155

    申请日：2024-12-09

    Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    Inventor： Yixiong XIAO ,  Jingbo ZHOU

    IPC: G06F16/242

    Abstract: Data query method and apparatus based on large model, an electronic device, and a storage medium are disclosed, which relates to the field of artificial intelligence, specifically in natural language processing, deep learning, and large model technologies, applicable to scenarios such as dialogue systems and information retrieval. The method includes: performing entity recognition on a query to obtain the target entity in the query; obtaining a first related content associated with the target entity from internal information, and performing data analysis on the first related content using a large language model (LLM) to obtain a data analysis result; obtaining a second related content associated with the target entity from external information, and performing data generation on the second related content using the LLM to obtain a data generation result; obtaining a query result corresponding to the query based on the data analysis result and the data generation result.

    DRUG REACTION PREDICTION AND MODEL TRAINING METHOD, APPARATUS AND DEVICE
    16.
    发明申请
    DRUG REACTION PREDICTION AND MODEL TRAINING METHOD, APPARATUS AND DEVICE 有权

    公开(公告)号：US20250014766A1

    公开(公告)日：2025-01-09

    申请号：US18895554

    申请日：2024-09-25

    Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

    Inventor： Jingbo ZHOU ,  Yuhan YE

    IPC: G16H70/40 ,  G06N20/00

    Abstract: A drug reaction prediction method, which is related to the field of artificial intelligence, specifically involving deep learning, computational biology, and chemistry, is disclosed. The drug reaction prediction method includes: obtaining a target graph based on multiple levels of entities contained in a drug to be predicted; the target graph includes an entity graph representing topological information within the entities and an interaction graph representing correlation information between the entities; performing representation extraction processing on the target graph to obtain an initial representation; obtaining a target representation based on a predetermined prompt identifier and the initial representation; and obtaining a drug reaction prediction result for the drug to be predicted based on the target representation.

    DATA UPDATING METHOD, MODEL TRAINING METHOD, APPARATUS, ELECTRONIC DEVICE AND MEDIUM
    17.
    发明公开
    DATA UPDATING METHOD, MODEL TRAINING METHOD, APPARATUS, ELECTRONIC DEVICE AND MEDIUM 审中-公开

    公开(公告)号：US20240282103A1

    公开(公告)日：2024-08-22

    申请号：US18654477

    申请日：2024-05-03

    Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

    Inventor： Congxi XIAO ,  Jizhou HUANG ,  Jingbo ZHOU

    IPC: G06V20/10 ,  G06V10/26 ,  G06V10/74 ,  G06V10/82

    CPC classification number: G06V20/176 ,  G06V10/26 ,  G06V10/761 ,  G06V10/82

    Abstract: A data updating method, a model training method and related devices are provided. The method includes obtaining urban graph data in a preset region, the urban graph data including a node set including central nodes, an edge set and a feature set, the edge set including neighborhoods corresponding to the central nodes, the neighborhoods including other nodes possessing connecting edges with the central nodes, the neighborhoods corresponding to a target region, and the feature set including node features of the nodes in the node set; partitioning the target region into at least two sub-regions to obtain a region partition set; aggregating the node features corresponding to all nodes located within the same sub-region to obtain the regional features of each of the sub-regions; updating the node features of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.

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