Roku, Inc.

Imagine Communications Corp.

Amit Verma

Fei Xiao

Abhishek Bambha

Rohit Mahto

Ronica Jethwa

Nam Vo

Zidong Wang

John Tinsman

Jose Sanchez

Pulkit Aggarwal

Lian Liu

Rameen Mahdavi

Atishay Jain

Jean-Pol Zundel

Mark Crouse

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.

Generating short-form content from full-length media using a machine learning model

Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history. The embodiment then generates, using the trained conversational recommendation system, a real-time recommendation having high play probability based on the minimal number of iterations of conversation between a user and the trained conversational recommendation system.

Interest-based conversational recommendation system

In example embodiments, a system and method for dynamically scheduling advertising is provided. For example, an advertisement order that describes an advertising campaign in logical terms is received. Stored data of statistically sampled metrics associated with previously distributed programs may be accessed. Based on the stored data and requirements indicated in the advertisement order, the logical terms of the advertising campaign may be translated into physical parameters adapted to facilitate placement of advertisements into an advertising schedule. The physical parameters of the advertising campaign may be provided to a device for creating the advertising schedule.

Dynamic scheduling for advanced advertising in linear television

Growing community of inventors

Sunnyvale, CA, United States of America

Amit Verma