公开(公告)号：US20190236157A1

公开(公告)日：2019-08-01

申请号：US15884064

申请日：2018-01-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Nikhil Agarwal ,  Pankhuri Goyal ,  Sachin Kakkar

IPC: G06F17/30

CPC classification number: G06F16/24578 ,  G06F16/283 ,  G06F16/9535

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of questions from a community of members. Next, the system uses a set of tags for each question and member attributes for a member of the community to calculate a relevance score representing a relevance of the question to the member. The system then combines the relevance score with additional scores for the member to obtain an overall score between the question and the member. Finally, the system ranks the set of questions by the overall scores and outputs the ranked set of questions to the member for use in obtaining answers to the set of questions from the member.

公开(公告)号：US20230351247A1

公开(公告)日：2023-11-02

申请号：US17735020

申请日：2022-05-02

Applicant: Microsoft Technology Licensing, LLC

Inventor： Boyi Chen ,  Tong Zhou ,  Siyao Sun ,  Lijun Peng ,  Xinruo Jing ,  Vakwadi Thejaswini Holla ,  Yi Wu ,  Pankhuri Goyal ,  Souvik Ghosh ,  Zheng Li ,  Yi Zhang ,  Onkar A. Dalal ,  Jing Wang ,  Aarthi Jayaram

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Embodiments of the disclosed technologies receive a first-party trained model and a first-party data set from a first-party system into a protected environment, receive a first third-party data set into the protected environment, and, in a data clean room, joining the first-party data set and the first third-party data set to create a joint data set for the particular segment, tuning a first-party trained model with the joint data set to create a third-party tuned model, sending model parameter data learned in the data clean room as a result of the tuning to an aggregator node, receiving a globally tuned version of the first-party trained model from the aggregator node, applying the globally tuned version of the first-party trained model to a second third-party data set to produce a scored third-party data set, and providing the scored third-party data set to a content distribution service of the first-party system.