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    METHOD FOR PROVIDING INFORMATION BASED ON E-COMMERCE AND COMPUTING DEVICE FOR EXECUTING THE SAME
    41.
    发明公开
    METHOD FOR PROVIDING INFORMATION BASED ON E-COMMERCE AND COMPUTING DEVICE FOR EXECUTING THE SAME 审中-公开

    公开(公告)号：US20240257195A1

    公开(公告)日：2024-08-01

    申请号：US18422281

    申请日：2024-01-25

    Applicant: CONIALAB CO., LTD.

    Inventor： HYUN JUNG NA

    IPC: G06Q30/0601 ,  G06Q30/0201

    CPC classification number: G06Q30/0601 ,  G06Q30/0201 ,  G06Q30/0631

    Abstract: A method for providing information based on e-commerce according to an embodiment of the present disclosure is performed on a computing device including one or more processors and a memory that stores one or more programs executed by the one or more processors, and includes collecting product sales information of each seller registered on a platform, collecting purchase activity information of a purchaser who accesses an online shopping mall of each seller, generating a product sales-related report for each supplier supplying a product to the platform based on the product sales information and the purchase activity information, and providing the product sales-related report to a corresponding supplier terminal.

    SYSTEM AND METHOD FOR AUTOMATICALLY PROVIDING RELEVANT DIGITAL ADVERTISEMENTS
    42.
    发明公开
    SYSTEM AND METHOD FOR AUTOMATICALLY PROVIDING RELEVANT DIGITAL ADVERTISEMENTS 审中-公开

    公开(公告)号：US20240257182A1

    公开(公告)日：2024-08-01

    申请号：US18103107

    申请日：2023-01-30

    Applicant: Walmart Apollo, LLC

    Inventor： Fanglida Yan ,  Tanay Kumar Saha ,  Musen Wen

    IPC: G06Q30/0251 ,  G06Q30/0201 ,  G06Q30/0273

    CPC classification number: G06Q30/0256 ,  G06Q30/0201 ,  G06Q30/0275

    Abstract: Systems and methods for automatically determining and providing digital advertisements with ensured relevancy to a search query are disclosed. In some embodiments, based on historical user session data, a query is determined to be a head query, torso query, or tail query. For each sponsored item, a relevance score is generated to represent a degree of relevancy between the sponsored item and the query, and it is determined whether the sponsored item is eligible to be recommended in response to the query based on: (a) comparing the relevance score to a first threshold when the query is a head or torso query, or (b) comparing the relevance score to a second threshold when the query is a tail query. Based on all sponsored items that are eligible to be recommended, a ranked list of recommended items is generated for display based on an auction mechanism.

    CLASSIFYING NODES OR EDGES OF GRAPHS BASED ON NODE EMBEDDINGS AND EDGE EMBEDDINGS
    43.
    发明公开
    CLASSIFYING NODES OR EDGES OF GRAPHS BASED ON NODE EMBEDDINGS AND EDGE EMBEDDINGS 审中-公开

    公开(公告)号：US20240257160A1

    公开(公告)日：2024-08-01

    申请号：US18138962

    申请日：2023-04-25

    Applicant: Salesforce, Inc.

    Inventor： Akash Singh ,  Rajdeep Dua

    IPC: G06Q30/0201 ,  G06Q30/018

    CPC classification number: G06Q30/0201 ,  G06Q30/0185

    Abstract: A method or a system for predicting a likelihood of an occurrence of a transaction. The system accesses a graph including multiple nodes and multiple edges linking the nodes. The multiple nodes include a first type of nodes representing a first type of entities an a second type of nodes representing a second type of entities. The system extract a set of node features for each node, and a set of edge features for each edge. For an edge connecting a first node of the first type and a second node of the second type, the system generates a set of edge embeddings based in part on the node features and edge features, and computes a score based in part on the set of edge embeddings. The score indicates a likelihood of an occurrence of a transaction between the first node and the second node.

    Generation and use of topic graph for content authoring
    44.
    发明授权
    Generation and use of topic graph for content authoring 有权

    公开(公告)号：US12050612B2

    公开(公告)日：2024-07-30

    申请号：US17895863

    申请日：2022-08-25

    Applicant: Graphite Growth, Inc.

    Inventor： Jose Luis Paredes ,  Marcos Ciarrocchi ,  Gregory Druck

    IPC: G06F16/2457 ,  G06F16/28 ,  G06Q30/0201

    CPC classification number: G06F16/24578 ,  G06F16/285 ,  G06Q30/0201

    Abstract: A system generates a topic graph based on the SERP data for high-ranking keywords in a search engine. Clustering may be based on (for example) degrees of intersection between links in search results of keywords from the SERP data, or keyword embeddings on the SERP data. The topic graph loosely clusters the keywords, such that the keywords have at least a threshold degree of similarity to their clusters, but not necessarily to all the other keywords in the cluster. As a consequence of the loose clustering, a given topic contains keywords that represent different aspects of the same concept, such that a content viewer would likely be interested in a piece of content that addresses the different aspects, and a search engine would be more likely to highly rank the content within its search results for one of the keywords. The system may also provide a user interface permitting a user to browse and filter the topics in the topic graph according to search criteria, as well as to see the topics ordered according to topic ROI estimates computed by the system.

    Efficient Feature Engineering for Recommender Systems
    45.
    发明公开
    Efficient Feature Engineering for Recommender Systems 审中-公开

    公开(公告)号：US20240249296A1

    公开(公告)日：2024-07-25

    申请号：US18158597

    申请日：2023-01-24

    Applicant: The Boston Consulting Group, Inc.

    Inventor： Davide Guatta ,  Alex Bergamasco

    IPC: G06Q30/0201

    CPC classification number: G06Q30/0201

    Abstract: Described is a recommender engine where a first plurality of customers prospects is exposed to a second plurality of potential actions, the recommender engine filters tuples of customers and actions according to one or more applied business rules, generates features that identify a primary key that characterizes a specific feature to determine a minimum level of representation to eliminate redundancy, the feature generator executes a feature calculation to fit feature values per each primary key that are computed to subsequently reconstruct the feature per each primary key, transforms the features to return the feature values according to a number of primary keys that needs to be fetched, composes a feature matrix that includes a portion of the primary keys that needs to be fetched, scores the portion of the primary keys from feature matrix, and issues recommendations for tuples of customers and actions according to the feature matrix.

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