Microsoft Technology Licensing, LLC

Christopher Ian Charla

Gershom L Payzer

Madeline Jaye Whisenant

Jennifer Rose Guriel

Bridgette Marie Kuehn

Kathryn Stone Perez

William B Dolan

Christopher Brian Quirk

Christopher John Brockett

David Alexander Molnar

Robert Alexander Sim

Vikram Reddy Dendi

Pallavi Choudhury

Scott Laufer

Noelle Mallory Sophy

Jianxun Lian

Mingqi Wu

Licheng Pan

Abdelrahman Khaled Abdo Mohamed

Nicole Beaudry

Thomas E Woolsey

Lily Wang

This disclosure relates to a content cluster system that provides a framework for leveraging large language models (LLMs) to tag content items (e.g., interactive multimedia content items, such as gaming content items) with attribute tags and, based on attribute tags for related content items, generating cluster descriptions of a cluster of content items. Features of the content cluster system involve tagging content items, determining multi-dimensional embeddings for the content items, and clustering the content items based on proximity of values contained within the multi-dimensional embeddings. The content cluster system may further utilize the LLM to generate one or more cluster descriptions based on the associated tags to create a more creative and dynamic representation of related groupings of titles (e.g., gaming titles). By utilizing LLMs and machine learning resources, the content cluster system provides a scalable approach to conventional approaches in determining and presenting groupings of titles.

Systems and methods for clustering digital content

A method of recommending an unplayed game application to a user includes obtaining a game history associated with a user profile, wherein the game history includes at least one game record of a game application the user profile has been used to play, and evaluating the game history to obtain a plurality of game events associated with a game application of the game history. The method further includes accessing a table of tags associated with the plurality of game events associated with the game application, aggregating a plurality of tags in a tag list in the user profile based at least partially on the table of tags and the plurality of game events, and presenting to the user a recommendation of an unplayed game application based at least partially on the plurality of tags.

Systems and methods for rich personalization of computer game experiences

A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.

Generating stimuli for use in soliciting grounded linguistic information

Growing community of inventors

Seattle, WA, United States of America

Christopher Ian Charla