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US 2024/0403623 A1Published application
Generative artificial intelligence for embeddings used as inputs to machine learning models
AssigneeMicrosoft Technology Licensing, LLC
InventorsRafikah Binte Mohamed Halim, Vinay Praneeth Boda, Sirou Zhu
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Abstract
In an example embodiment, a generative artificial intelligence (GAI) model is used to generate embeddings, eliminating the need for a separately trained embedding model or layer. These embeddings may then be used as input to another machine learning model. In some example embodiments, these embeddings are generated on interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms.
Claims20
A system comprising:
- at least one processor;
- and
- at least one non-transitory computer-readable medium having instructions stored thereon, which, when executed by the at least one processor, cause the system to perform operations comprising:
- accessing interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms;
- based on the interaction data and profile data associated with the user, predicting a journey phase of a user journey for the user, the journey phase indicative of user intent to perform a first type of interaction with a first online platform;
- accessing a first piece of content presented on the first online platform;
- feeding the first piece of content into a generative artificial intelligence (GAI) model, the GAI outputting an embedding corresponding to the first piece of content, the embedding being a representation of a meaning of the content;
- feeding the journey phase and the embedding into a machine learning model trained separately from the GAI model, the machine learning model outputting a score for the first piece of content;
- and
- determining whether to cause the first piece of content to be presented to the user via the first online platform based on the score.
7 more elements
A method comprising:
- accessing interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms;
- based on the interaction data and profile data associated with the user, predicting a journey phase of a user journey for the user, the journey phase indicative of user intent to perform a first type of interaction with a first online platform;
- accessing a first piece of content presented on the first online platform;
- feeding the first piece of content into a generative artificial intelligence (GAI) model, the GAI outputting an embedding corresponding to the first piece of content, the embedding being a representation of a meaning of the content;
- feeding the journey phase and the embedding into a machine learning model trained separately from the GAI model, the machine learning model outputting a score for the first piece of content;
- and
- determining whether to cause the first piece of content to be presented to the user via the first online platform based on the score.
4 more elements
A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:
- accessing interaction data regarding one or more interactions between a user and digital content presented on one or more online platforms;
- based on the interaction data and profile data associated with the user, predicting a journey phase of a user journey for the user, the journey phase indicative of user intent to perform a first type of interaction with a first online platform;
- accessing a first piece of content presented on the first online platform;
- feeding the first piece of content into a generative artificial intelligence (GAI) model, the GAI outputting an embedding corresponding to the first piece of content, the embedding being a representation of a meaning of the content;
- feeding the journey phase and the embedding into a machine learning model trained separately from the GAI model, the machine learning model outputting a score for the first piece of content;
- and
- determining whether to cause the first piece of content to be presented to the user via the first online platform based on the score.
4 more elements
Dependencies are inferred from the claim text.