公开(公告)号：US20240214630A1

公开(公告)日：2024-06-27

申请号：US18435171

申请日：2024-02-07

Applicant: Roku, Inc.

Inventor： Fei Xioa ,  Ronica Jethwa ,  Zidong Wang ,  Jing Lu ,  Jing Ye ,  Nam Vo ,  Jose Sanchez ,  Abhishek Bambha ,  Khaldun Aidarabsah

IPC: H04N21/433 ,  G06F16/75 ,  H04N21/45

CPC classification number: H04N21/4332 ,  G06F16/75 ,  H04N21/4532

Abstract: 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.

公开(公告)号：US11941067B1

公开(公告)日：2024-03-26

申请号：US17943526

申请日：2022-09-13

Applicant: ROKU, INC.

Inventor： Fei Xiao ,  Ronica Jethwa ,  Zidong Wang ,  Jing Lu ,  Jing Ye ,  Nam Vo ,  Jose Sanchez ,  Abhishek Bambha ,  Khaldun Aidarabsah

IPC: G06F16/906

CPC classification number: G06F16/906

Abstract: 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.

公开(公告)号：US12235905B2

公开(公告)日：2025-02-25

申请号：US18435171

申请日：2024-02-07

Applicant: Roku, Inc.

Inventor： Fei Xiao ,  Ronica Jethwa ,  Zidong Wang ,  Jing Lu ,  Jing Ye ,  Nam Vo ,  Jose Sanchez ,  Abhishek Bambha ,  Khaldun Aidarabsah

IPC: G06F16/906 ,  G06F16/75 ,  H04N21/433 ,  H04N21/45

Abstract: 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.

公开(公告)号：US20240273575A1

公开(公告)日：2024-08-15

申请号：US18108090

申请日：2023-02-10

Applicant: ROKU, INC.

Inventor： ABHISHEK BAMBHA ,  Weicong Ding ,  Ronica Jethwa ,  Rohit Mahto ,  Abhishek Majumdar ,  Amit Verma ,  Zidong Wang ,  Fei Xiao

IPC: G06Q30/0251

CPC classification number: G06Q30/0269 ,  G06Q30/0261

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing user experience/engagement and revenue. An example embodiment operates by a computer-implemented method for providing one or more advertisements to a media device. The method includes receiving, by at least one computer processor, a user state associated with a user of the media device, where the user state corresponds to a time step. The method further includes receiving a revenue value associated with the user of the media device, where the revenue value corresponds to the time step. The method also include determining an action associated with the user based on the user state and the revenue value. The action includes one or more parameters associated with the one or more advertisements. The method further includes providing the action to the user.

公开(公告)号：US12219190B2

公开(公告)日：2025-02-04

申请号：US17890491

申请日：2022-08-18

Applicant: ROKU, INC.

Inventor： Fei Xiao ,  Zidong Wang ,  Jose Sanchez ,  Abhishek Bambha ,  Ronica Jethwa

IPC: H04N21/25 ,  H04N21/258

Abstract: 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.

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.