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    Emotion evaluation of contents
    2.
    发明授权
    Emotion evaluation of contents 有权

    公开(公告)号：US12301897B2

    公开(公告)日：2025-05-13

    申请号：US18425803

    申请日：2024-01-29

    Applicant: Roku, Inc.

    Inventor： Ronica Jethwa ,  Nam Vo ,  Fei Xiao ,  Abhishek Bambha

    IPC: H04N21/234 ,  G06V20/40 ,  H04N21/25 ,  H04N21/81 ,  H04N21/8549

    Abstract: 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.

    CONTENT ACQUISITION SYSTEM
    3.
    发明申请
    CONTENT ACQUISITION SYSTEM 有权

    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.

    Media device user interface and content personalization using natural language prompts
    4.
    发明授权
    Media device user interface and content personalization using natural language prompts 有权

    公开(公告)号：US12282784B1

    公开(公告)日：2025-04-22

    申请号：US18486515

    申请日：2023-10-13

    Applicant: ROKU, INC.

    Inventor： Atishay Jain ,  Fei Xiao ,  Abhishek Bambha ,  Mehul Agrawal ,  Rohit Mahto

    IPC: G06F3/048 ,  G06F9/451 ,  G06F40/30 ,  G10L15/26

    Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for personalizing a user interface (UI) of a media device and/or content presented thereby. An example embodiment operates by obtaining a first natural language user input, providing the first natural language user input to a personalization language model that is configured to interpret different natural language user inputs to respectively determine different update tasks invoked thereby, the different update tasks including a UI update task and a content update task, receiving from the model a first update task determined thereby based at least on the first natural language user input, generating one or more first application programming interface (API) calls based on the first update task, and placing the one or more first API calls to a service that implements the first update task based on the one or more first API calls.

    MEDIA DEVICE USER INTERFACE AND CONTENT PERSONALIZATION USING NATURAL LANGUAGE PROMPTS
    5.
    发明申请
    MEDIA DEVICE USER INTERFACE AND CONTENT PERSONALIZATION USING NATURAL LANGUAGE PROMPTS 有权

    公开(公告)号：US20250123857A1

    公开(公告)日：2025-04-17

    申请号：US18486515

    申请日：2023-10-13

    Applicant: ROKU, INC.

    Inventor： Atishay Jain ,  Fei Xiao ,  Abhishek Bambha ,  Mehul Agrawal ,  Rohit Mahto

    IPC: G06F9/451 ,  G06F40/30 ,  G10L15/26

    Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for personalizing a user interface (UI) of a media device and/or content presented thereby. An example embodiment operates by obtaining a first natural language user input, providing the first natural language user input to a personalization language model that is configured to interpret different natural language user inputs to respectively determine different update tasks invoked thereby, the different update tasks including a UI update task and a content update task, receiving from the model a first update task determined thereby based at least on the first natural language user input, generating one or more first application programming interface (API) calls based on the first update task, and placing the one or more first API calls to a service that implements the first update task based on the one or more first API calls.

    CONTENT DISPLAY AND CLUSTERING SYSTEM
    6.
    发明公开
    CONTENT DISPLAY AND CLUSTERING SYSTEM 审中-公开

    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.

    Content display and clustering system
    8.
    发明授权
    Content display and clustering system 有权

    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.

    Recommendation system with reduced bias based on a view history
    9.
    发明授权
    Recommendation system with reduced bias based on a view history 有权

    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.

    Interest-based conversational recommendation system
    10.
    发明授权
    Interest-based conversational recommendation system 有权

    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.

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