公开(公告)号：US20250061507A1

公开(公告)日：2025-02-20

申请号：US18722879

申请日：2023-01-04

Applicant: BEIJING YOUZHUJU NETWORK TECHNOLOGY CO., LTD. ,  LEMON INC.

Inventor： Yuanshun YAO ,  Chong WANG ,  Hang LI

IPC: G06Q30/0601

Abstract: The present disclosure provides a method for obtaining a recommended explanation, a device, and a computer readable medium. The method includes: generating a recommended item by a recommendation model; calculating a similarity between a plurality of explanatory items and the recommended item; obtaining a predetermined number of explanatory items from the plurality of explanatory items, as a recommended explanation of the recommended item, wherein a similarity between the predetermined number of explanatory items and the recommended item is greater than a similarity between other explanatory items and the recommended item; and outputting identification information of the predetermined number of explanatory items.

公开(公告)号：US20250148111A1

公开(公告)日：2025-05-08

申请号：US18837078

申请日：2023-02-02

Applicant: Lemon Inc.

Inventor： Jiankai SUN ,  Xin YANG ,  Yuanshun YAO ,  Junyuan XIE ,  Chong WANG

IPC: G06F21/62

Abstract: A method and apparatus for protecting data, and a device and a medium are provided. The method comprises acquiring, by a first device, a feature representation generated by a second device on the basis of sample data and according to a second model, wherein the first device has label information for the sample data; and the first device and the second device are used for jointly training a first model at the first device and the second model at the second device. The method further comprises generating, by the first device, a prediction label for the sample data on the basis of the feature representation and according to the first model. The method further comprises determining, by the first device and on the basis of the feature representation, the label information and the predicted label, a total loss value used for training the first model and the second model.

公开(公告)号：US20240126899A1

公开(公告)日：2024-04-18

申请号：US18539851

申请日：2023-12-14

Applicant: Lemon Inc.

Inventor： Xin YANG ,  Junyuan XIE ,  Jiankai SUN ,  Yuanshun YAO ,  Chong WANG

IPC: G06F21/62 ,  G06N3/04 ,  G06N3/098

CPC classification number: G06F21/62 ,  G06N3/04 ,  G06N3/098

Abstract: There are proposed a method, device, apparatus, and medium for protecting sensitive data. In a method, to-be-processed data is received from a server device. A processing result of a user for the to-be-processed data is received, the processing result comprising sensitive data of the user for the processing of the to-be-processed data. A gradient for training a server model at the server device is determined based on a comparison between the processing result and a prediction result for the to-be-processed data. The gradient is updated in a change direction associated with the gradient so as to generate an updated gradient to be sent to the server device. Noise is added only in the change direction associated with the gradient. The corresponding overhead of processing noise in a plurality of directions can be reduced, and no excessive noise data interfering with training will be introduced to the updated gradient.

公开(公告)号：US20240119341A1

公开(公告)日：2024-04-11

申请号：US17953255

申请日：2022-09-26

Applicant: Lemon Inc.

Inventor： Xin YANG ,  Hanlin ZHU ,  Tianyi LIU ,  Jiankai SUN ,  Yuanshun YAO ,  Aonan ZHANG ,  Chong WANG

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The present disclosure describes techniques for determining performance of a classifier. A first machine learning model and a second machine learning model may be trained by aggregating updates to the first machine learning model and the second machine learning model received from a plurality of client computing devices. A cumulative distribution function (CDF) associated with a distribution of the positive samples in the user data may be estimated using the trained first machine learning model. A probability density function (PDF) associated with a distribution of the negative samples in the user data may be estimated using the trained second machine learning model. An integration-based computation of an area under the receiver operating characteristic curve (AUC) of the classifier may be performed using the PDF and the CDF.