Roku, Inc.

Ronica Jethwa

Fei Xiao

Abhishek Bambha

Nam Vo

Zidong Wang

Sunil Ramesh

Michael Cutter

Rohit Mahto

Jose Sanchez

Lian Liu

Jing Ye

Karina Levitian

Khaldun Aidarabsah

Pulkit Aggarwal

Jing Lu

Amit Verma

Rameen Mahdavi

Atishay Jain

Anirban Das

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

In one aspect, an example method in connection with a shared content viewing session in which multiple users are receiving and viewing respective instances of the same media content in a synchronized manner is disclosed. The example method includes (i) detecting that each of the multiple users respectively exhibits a threshold extent of physical reaction around the same time; (ii) responsive to the detecting, for each of the multiple users, generating and/or storing respective visual content representing that user's physical reaction; (iii) generating a visual content composition that includes at least the generated and/or stored visual content for each of the multiple users; and (iv) outputting for presentation, the generated visual content composition.

Method and system for generating a visual composition of user reactions in a shared content viewing session

A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

Interest based recommendation system

A method and system for controlling media-content presentation based on user presence and/or user profile. An example method includes a computing system determining a quantity of users present at a media-presentation device, the quantity being at least one. Further, the example method includes, based on the determining, the computing system using the determined quantity of users present at the media-presentation device as a basis to control what media content a media player outputs for presentation by the media-presentation device. For instance, based on the determined quantity of users the computing system could tailor a graphical user interface (GUI) that the media player outputs for presentation by the media-presentation device, such as by tailoring a set of channel options that a channel-selection GUI provides, among other possibilities.

Dynamic control of media-content presentation based on user presence and/or user profile

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a scene emotion value for a scene based on a sequence of frame emotion values for a sequence of frames within the scene of a content. The content can include multiple scenes, and a scene can include multiple frames, where a frame emotion value can be generated for each frame. A frame emotion value can be generated based on scene metadata related to the scene, content metadata related to the content, and a frame metadata related to the frame.

Emotion evaluation of contents

Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Content display and clustering system

Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Recommendation system with reduced bias based on a view history

A method and system for controlling media-content presentation. An example method includes a computing system detecting that a media-player device has moved from a home location of the media-player device to a visited location and that the media-player device is connected with a media-presentation device at the visited location. Further, the example method includes, responsive to the detecting, the computing system causing the media-player device to output, for presentation by the media-presentation device at the visited location, media content selected by the computing system based on the media-player device having moved from the home location to the visited location. For instance, the computing system could cause the media-player device to output, for presentation by the media-presentation device, a message welcoming a user to the visited location and/or information about the visited location such as information about tourist attractions at the visited location for instance.

Controlling media-content presentation based on movement of media-player device from home location to visited location

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

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating trailers (previews) for multimedia content. An example aspect operates by generating an initial set of candidate points to generate a trailer for a media content; determining conversion data for each of the initial set of candidate points; determining an updated set of candidate points based on the conversion data; determining an estimated mean and upper bound for each of the updated set of candidate points; computing a value for each of the updated set of candidate points; generating a ranked list based on the value computed for each of the updated set of candidate points; and repeating the process until an optimal candidate point is converged upon.

Growing community of inventors

Mountain View, CA, United States of America

Ronica Jethwa