Systems and methods for scene change recommendations
AssigneeAdeia Guides Inc.
InventorsVishwas Sharadanagar Panchaksharaiah, Vikram Makam Gupta
Life of this patent
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Abstract
Systems and methods are disclosed for indicating whether a scene in a media asset corresponds to a scene for which a scene change was previously requested. A media player client monitors scene sequences in a media asset to anticipate whether the user will initiate a scene change request based on historical scene change data. In response to detecting a scene sequence for which the user is expected to request a scene change, the media player client determines whether a scene change is necessary and outputs an indication of whether a scene change is recommended.
Claims20
A method comprising:
- training, based on a plurality of training data structures of a user profile, a machine learning model to classify a scene, wherein the machine learning model is configured to:
- receive an input data structure;
- and
- output an output binary classifier indicating whether a scene change is recommended;
- receiving a first scene change request for a first scene of a media asset;
- generating a first data structure, wherein the first data structure indicates metadata for the first scene;
- inputting the first data structure into the machine learning model;
- and
- receiving a binary operator indicating that the first scene should not be skipped.
A system comprising:
- control circuitry configured to:
- train, based on a plurality of training data structures of a user profile, a machine learning model to classify a scene, wherein the machine learning model is configured to:
- receive an input data structure;
- and
- output an output binary classifier indicating whether a scene change is recommended;
- input/output circuitry configured to:
- receive a first scene change request for a first scene of a media asset;
- the control circuitry further configured to:
- generate a first data structure, wherein the first data structure indicates metadata for the first scene;
- input the first data structure into the machine learning model;
- and
- receive a binary operator indicating that the first scene should not be skipped.
Dependencies are inferred from the claim text.