公开(公告)号：US20250016425A1

公开(公告)日：2025-01-09

申请号：US18829979

申请日：2024-09-10

Applicant: ROKU, INC.

Inventor： Fei XIAO ,  Abhishek Bambha ,  Nam Vo ,  Pulkit Aggarwal ,  Rohit Mahto ,  Andrey Vlasenko ,  Rameen Mahdavi

IPC: H04N21/81 ,  G06Q30/0241 ,  G06Q30/0251 ,  H04N21/25 ,  H04N21/254

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.

公开(公告)号：US12155878B2

公开(公告)日：2024-11-26

申请号：US18076955

申请日：2022-12-07

Applicant: ROKU, INC.

Inventor： Pulkit Aggarwal ,  Abhishek Bambha ,  Rohit Mahto ,  Nam Vo ,  Fei Xiao

IPC: H04N21/25 ,  H04N21/258 ,  H04N21/466

Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.

公开(公告)号：US12126874B2

公开(公告)日：2024-10-22

申请号：US18091234

申请日：2022-12-29

Applicant: ROKU, INC.

Inventor： Fei Xiao ,  Abhishek Bambha ,  Nam Vo ,  Pulkit Aggarwal ,  Rohit Mahto ,  Andrey Vlasenko ,  Rameen Mahdavi

IPC: H04N21/81 ,  G06Q30/0241 ,  G06Q30/0251 ,  H04N21/25 ,  H04N21/254

CPC classification number: H04N21/812 ,  G06Q30/0249 ,  G06Q30/0254 ,  H04N21/251 ,  H04N21/2542

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.

公开(公告)号：US20250086687A1

公开(公告)日：2025-03-13

申请号：US18464617

申请日：2023-09-11

Applicant: Roku, Inc.

Inventor： Jin Bao ,  Pulkit Aggarwal ,  Nam Vo ,  Zhiwei Chen ,  Shashank C. Merchant

IPC: G06Q30/0601 ,  G06F40/40 ,  G06Q30/0251 ,  G06V10/764

Abstract: A method and system for processing a purchase based on image recognition in a video stream being presented by a computing system. A method includes receiving a first user-input defining a first user-request to pause presentation of the video stream, and, responsive to the first user-input, pausing by the computing system the presentation of the video stream at a video frame. Further, the method includes detecting based on computer-vision analysis of the video frame, at least one object depicted by the video frame. Additionally, the method includes correlating the detected object with at least one purchasable item and presenting a prompt for purchase of the at least one purchasable item. Also, the method includes receiving a second user-input requesting to purchase a given one of the at least one purchasable item and processing, responsive to receiving the second user-input, a purchase of the given purchasable item for the user.

公开(公告)号：US12190864B1

公开(公告)日：2025-01-07

申请号：US18734961

申请日：2024-06-05

Applicant: Roku, Inc.

Inventor： Fei Xiao ,  Amit Verma ,  Rohit Mahto ,  Rameen Mahdavi ,  Nam Vo ,  Zidong Wang ,  Lian Liu ,  Jose Sanchez ,  Pulkit Aggarwal ,  Atishay Jain ,  Abhishek Bambha ,  Ronica Jethwa

IPC: G10L15/06

Abstract: 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.