At&t Intellectual Property I, L.p.

Subhabrata Majumdar

Eric Martin Zavesky

Rajat Malik

Mohammed Abdel-Wahab

Jean-Francois Paiement

Jianxiong Dong

James H Pratt

David Crawford Gibbon

Tan Xu

Richard Palazzo

Wen-Ling Hsu

Deirdre Paul

Louis E Alexander

Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Sequential decision analysis techniques for e-sports

A system for embedding users' preferences and behaviors based on interactions with an array of devices.

Cognitive persona embeddings for secure omni-channel personalized recommendations

In one example, a method performed by a processing system including at least one processor includes acquiring a first item of media content from a user, where the first item of media content depicts a subject, acquiring a second item of media content, where the second item of media content depicts the subject, compositing the first item of media content and the second item of media content to create, within a metaverse of immersive content, an item of immersive content that depicts the subject, presenting the item of immersive content on a device operated by the user, and adapting the presenting of the item of immersive content in response to a choice made by the user.

Compositing non-immersive media content to generate an adaptable immersive content metaverse

An example method includes obtaining a plurality of candidate media segments for possible inclusion in a single stream of media segments that is personalized for a first user, wherein at least one candidate media segment of the plurality of candidate media segments comprises an excerpt from a media asset, selecting, based on a known media consumption behavior of the first user, a subset of the plurality of candidate media segments, wherein the subset includes candidate media segments of the plurality of candidate media segments that are to be included in the single stream of media segments, modifying at least one candidate media segment in the subset based on the known media consumption behavior of the first user, and compiling the subset into the single stream of media segments, wherein the single stream of media segments includes the at least one candidate media segment in the subset that was modified.

Automated, user-driven, and personalized curation of short-form media segments

Aspects of the subject disclosure may include, for example, a machine-readable medium that has recorded executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, including analyzing media content for video complexity and audio characteristics, thereby creating a content classification; determining a user context for a user observing the media content; determining a content manipulation based on the content classification, the user context, or a combination thereof; and presenting the media content by applying the content manipulation determined. Other embodiments are disclosed.

System and method for dynamic manipulation of content presentation

Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.

Growing community of inventors

Jersey City, NJ, United States of America

Subhabrata Majumdar