公开(公告)号：US20230350698A1

公开(公告)日：2023-11-02

申请号：US17731939

申请日：2022-04-28

Applicant: Lemon Inc.

Inventor： Xiangyu Zeng ,  Yuan Gao ,  Zihe Xu ,  Hongyu Xiong ,  Han Wang ,  Bin Liu

IPC: G06F9/448 ,  G06N20/00 ,  G06K9/62

CPC classification number: G06F9/4488 ,  G06N20/00 ,  G06K9/6228 ,  G06K9/6256

Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products for event processing. In the method, first data associated with a first time window is obtained, the first data comprising a first object and a first group of events that are related to the first object. Second data associated with a second time window is obtained, the second data comprising a second object and a second group of events that are related to the second object, the second time window being different from the first time window. An event model describing an association relationship between an object and an event that is related to the object is determined based on the first and second data. With these implementations, multiple time windows are used in determining the event model, and thus the event model may have better performance in accuracy and immediacy aspects.

公开(公告)号：US12260628B2

公开(公告)日：2025-03-25

申请号：US17724140

申请日：2022-04-19

Applicant: Lemon Inc.

Inventor： Xiangyu Zeng ,  Hongyu Xiong ,  Yiqi Feng ,  Han Wang ,  Yuan Gao ,  Qingyi Lu ,  Rui Li

IPC: G06V10/80 ,  G06V10/22 ,  G06V10/40 ,  G06V20/40 ,  G06V20/62 ,  G06V30/14 ,  G10L15/26

Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products of extracting a feature for multimedia data that comprises a plurality of medium types. In a method, a first feature is determined for a first medium type in the plurality of medium types by masking a portion in a first medium object with the first medium type. A second feature is determined for a second medium type other than the first medium type in the plurality of medium types. The feature is generated for the multimedia data based on the first and second features. With these implementations, multiple medium types are considered in the feature extraction, and thus the feature may fully reflect various aspects of the multimedia data in an accurate way.

公开(公告)号：US12248884B2

公开(公告)日：2025-03-11

申请号：US18397227

申请日：2023-12-27

Applicant: Lemon Inc.

Inventor： Hongyu Xiong ,  Han Wang ,  Yuan Gao ,  Yiqi Feng ,  Bin Liu

IPC: G06N5/02 ,  G06F16/28

Abstract: The present disclosure relates to a method and apparatus for knowledge graph construction, storage medium and electronic device. The method for knowledge graph construction, comprises: identifying an entity concept from a title text of a target web page and at least one entity corresponding to the entity concept from a body text of the target web page; constructing a syntax parse tree of the title text based on syntax parse rules of a language to which the title text belongs, and determining, from the syntax parse tree, a modifier for modifying the entity concept; and generating a knowledge graph based on the entity concept, the modifier, and the at least one entity. Through the solution of the present disclosure, knowledge graphs with high accuracy and high recall rates are constructed without structured processing on target web pages.

公开(公告)号：US11675873B1

公开(公告)日：2023-06-13

申请号：US17809513

申请日：2022-06-28

Applicant: Lemon Inc.

Inventor： Han Wang ,  Hongyu Xiong ,  Zheng Chen ,  Tianyu Zhang ,  Yiqi Feng ,  Yuan Gao ,  Xiangyu Zeng ,  Rui Li ,  Qingyi Lu ,  Yihan Yang ,  Yu Zhang ,  Bin Liu

IPC: G06F16/958 ,  G06F16/954

CPC classification number: G06F16/958 ,  G06F16/954

Abstract: There are provided methods, devices, and computer program products for similarity determination. In a method, first and second access data are obtained for a first and a second group of users who access a first and a second website, respectively. A first and a second jump path are generated for the first and second groups of users based on the first and second access data, respectively. The first and second jump paths describe access history for the first and second groups of users among webpages in the first and second websites, respectively. A similarity is determined between the first and second websites based on the first and second jump paths. Here, access data are used for similarity determination and unvisited webpages are not considered in the similarity determination. Therefore, the computation workload may be lowered, and the noise caused by the unvisited webpages may be reduced.

公开(公告)号：US12204850B2

公开(公告)日：2025-01-21

申请号：US17728248

申请日：2022-04-25

Applicant: LEMON INC.

Inventor： Hongyu Xiong ,  Han Wang ,  Rui Li ,  Yiqi Feng ,  Yuan Gao ,  Xiangyu Zeng ,  Qingyi Lu ,  Bin Liu

IPC: G06F40/211 ,  G06F16/33 ,  G06F40/14 ,  G06F40/30

Abstract: Embodiments of the present disclosure relate to semantic parsing for short text. According to embodiments of the present disclosure, a method is proposed. The method comprises: obtaining a set of sentences associated with a short text, each of the set of sentences containing all of words in the short text; determining a set of syntactic features associated with the set of sentences, each of the set of syntactic features indicating at least one of a constituency relation and a dependency relation of the corresponding sentence; and determining a semantic structure of the short text based on the set of syntactic features.

公开(公告)号：US20230359824A1

公开(公告)日：2023-11-09

申请号：US17737918

申请日：2022-05-05

Applicant: Lemon Inc.

Inventor： Qingyi Lu ,  Yuan Gao ,  Hongyu Xiong ,  Han Wang ,  Bin Liu ,  Xiangyu Zeng ,  Rui Li ,  Yiqi Feng

IPC: G06F40/295 ,  G06F40/30 ,  G06F16/901

CPC classification number: G06F40/295 ,  G06F16/9024 ,  G06F40/30 ,  G06N20/00

Abstract: Embodiments of the present disclosure relate to feature crossing for machine learning. According to example embodiments of the present disclosure, a method comprises determining a semantic correlation relationship between a plurality of feature categories, the semantic correlation relationship indicating respective degrees of semantic correlation between respective pairs of feature categories among the plurality of feature categories; obtaining at least two features classified in at least two of the plurality of feature categories for machine learning; and performing feature crossing on the at least two features based on the semantic correlation relationship.