Microsoft Technology Licensing, LLC

Christopher Wright Lloyd, Ii

Jeffrey Gee

Mahesh Subhash Joshi

Gungor Polatkan

Shivani Rao

Konstantin Salomatin

Deepak Kumar Dileep Kumar

Christopher J Fong

Vladislav Tcheprasov

Aparna Krishnan

Suman Sundaresh

Lavish Shah

Muhammad Basit Khurram

Michaela Jillings

Jeremy K Owen

Embodiments of the disclosed technologies include generating a search prompt based on an input portion of an online dialog involving a user of a computing device. The search prompt includes a dialog summarization instruction configured to instruct a generative artificial intelligence model to generate and output a dialog summary. The search prompt is sent to a first generative model. In response to the search prompt, a search query is generated and output by the first generative model based on the dialog summary. The search query is sent to a search system. Search result data is determined based on an execution of the search query by the search system. At least some of the search result data is included in an output portion of the online dialog. The output portion is configured to be displayed at the computing device in response to the input portion of the online dialog.

Generative summarization dialog-based information retrieval system

Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Feature generation pipeline for machine learning

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Brooklyn, NY, United States of America

Christopher Wright Lloyd, Ii