公开(公告)号：US20240378424A1

公开(公告)日：2024-11-14

申请号：US18214905

申请日：2023-06-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Praveen Kumar Bodigutla ,  Suman Sundaresh ,  Souvik Ghosh ,  Saurabh Gupta ,  Sai Krishna Bollam ,  Arya Ghatak Choudhury ,  Weiheng Qian ,  Jiarui Wang

IPC: G06N3/0455 ,  G06N3/08

Abstract: Embodiments of the disclosed technologies include configuring a first machine learning model to generate and output suggested message content based on first correlations between message content and message acceptance data, where the first machine learning model includes a first encoder-decoder model architecture, configuring a second machine learning model to generate and output message evaluation data based on second correlations between the message content and the message acceptance data, where the second machine learning model includes a second encoder-decoder model architecture, coupling an output of the first machine learning model to an input of the second machine learning model, and coupling an output of the second machine learning model to an input of the first machine learning model.

公开(公告)号：US20240378425A1

公开(公告)日：2024-11-14

申请号：US18214939

申请日：2023-06-27

Applicant: Microsoft Technology Licensing, LLC

Inventor： Praveen Kumar Bodigutla ,  Suman Sundaresh ,  Souvik Ghosh ,  Saurabh Gupta ,  Sai Krishna Bollam ,  Arya Ghatak Choudhury ,  Weiheng Qian ,  Jiarui Wang

IPC: G06N3/0455 ,  G06N3/09

Abstract: Embodiments of the disclosed technologies include receiving first message attribute data and inputting the first message attribute data to a first machine learning model. The first machine learning model is configured to generate and output suggested message content based on first correlations between message content and message acceptance data. The first machine learning model generates a first set of message content suggestions based on the first message attribute data, and selects at least one message content suggestion from the first set of message content suggestions based on message evaluation data. Feedback data related to the selected at least one message content suggestion is received. The first machine learning model is tuned based on the feedback data. The tuned first machine learning model generates a second set of message content suggestions based on the first message attribute data.

公开(公告)号：US20250156641A1

公开(公告)日：2025-05-15

申请号：US18388726

申请日：2023-11-10

Applicant: Microsoft Technology Licensing, LLC

Inventor： Xueqian Tang ,  Lijun Peng ,  Jiarui Wang ,  Yi Zhang ,  Yi Wu ,  Arvind Subramaniam

IPC: G06F40/30

Abstract: In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.