System and method for dynamic manipulation of content presentation

Abstract

Aspects of the subject disclosure may include, for example, a machine-readable medium that has recorded executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, including analyzing media content for video complexity and audio characteristics, thereby creating a content classification; determining a user context for a user observing the media content; determining a content manipulation based on the content classification, the user context, or a combination thereof; and presenting the media content by applying the content manipulation determined. Other embodiments are disclosed.

What is claimed is: 
     
         1 . A device, comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
 analyzing media content for video characteristics and audio characteristics, thereby creating a content classification, wherein the video characteristics comprise motion in scenes and recurrence of characters in scenes, wherein the audio characteristics comprise speech rate and periods of silence; 
 determining characteristics of an environment where a user is viewing the media content, wherein the characteristics of the environment comprise a noise level of the environment; 
 retrieving a historical manipulation model for the media content, wherein the historical manipulation model includes a machine learning model trained using past content manipulations of the media content; 
 determining a content manipulation based on the content classification, the historical manipulation model, and the characteristics of the environment; 
 presenting the media content in accordance with the content manipulation; and 
 updating the historical manipulation model by further training the machine learning model using the content manipulation. 
   
     
     
         2 . The device of  claim 1 , wherein the operations further comprise:
 receiving user feedback responsive to the presenting of the media content; and   adjusting the presenting of the media content based on the user feedback.   
     
     
         3 . The device of  claim 1 , wherein the operations further comprise:
 applying a smoothing model to the content manipulation for the presenting of the media content, wherein the smoothing model adapts the content manipulation to be slowly splined into the media content.   
     
     
         4 . The device of  claim 1 , wherein the content manipulation comprises changing a rate of presentation, presenting a still frame, presenting a visual history, repeating the presenting of the media content, or a combination thereof. 
     
     
         5 . The device of  claim 1 , wherein the characteristics of the environment comprises a location of the user. 
     
     
         6 . The device of  claim 1 , wherein the historical manipulation model further includes a user preference for the content manipulation. 
     
     
         7 . The device of  claim 1 , wherein the content manipulation reduces a total time for the presenting of the media content. 
     
     
         8 . The device of  claim 7 , wherein the total time is reduced without clipping the media content. 
     
     
         9 . The device of  claim 1 , wherein the processing system comprises a plurality of processors operating in a distributed computing environment. 
     
     
         10 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
 analyzing media content for video characteristics and audio characteristics, thereby creating a content classification, wherein the video characteristics comprise motion in scenes and recurrence of characters in scenes, wherein the audio characteristics comprise speech rate and periods of silence;   determining characteristics of an environment where a user is viewing the media content, wherein the characteristics of the environment comprise a noise level of the environment;   retrieving a historical manipulation model for the media content, wherein the historical manipulation model includes a machine learning model trained using past content manipulations of the media content;   determining a content manipulation based on the content classification, the historical manipulation model, and the characteristics of the environment;   presenting the media content in accordance with the content manipulation; and   updating the historical manipulation model by further training the machine learning model using the content manipulation.   
     
     
         11 . The non-transitory machine-readable medium of  claim 10 , wherein the characteristics of the environment further comprise a location of the user. 
     
     
         12 . The non-transitory machine-readable medium of  claim 10 , wherein the content manipulation comprises changing a rate of presentation, presenting a still frame, presenting a visual history, repeating the presenting of the media content, or a combination thereof. 
     
     
         13 . The non-transitory machine-readable medium of  claim 10 , wherein the content manipulation reduces a total time for the presenting of the media content. 
     
     
         14 . The non-transitory machine-readable medium of  claim 10 , wherein the operations further comprise:
 receiving user feedback responsive to the presenting of the media content; and   adjusting the presenting of the media content based on the user feedback.   
     
     
         15 . The non-transitory machine-readable medium of  claim 10 , wherein the processor comprises a plurality of processors operating in a distributed computing environment. 
     
     
         16 . A method, comprising:
 analyzing, by a processing system including a processor, media content for video characteristics and audio characteristics, thereby creating a content classification, wherein the video characteristics comprise motion in scenes and recurrence of characters in scenes, wherein the audio characteristics comprise speech rate and periods of silence;   determining, by the processing system, characteristics of an environment where a user is viewing the media content, wherein the characteristics of the environment comprise a noise level of the environment;   retrieving, by the processing system, a historical manipulation model for the media content, wherein the historical manipulation model includes a machine learning model trained using past content manipulations of the media content;   determining, by the processing system, a content manipulation based on the content classification, the historical manipulation model, and the characteristics of the environment;   presenting, by the processing system, the media content in accordance with the content manipulation; and   updating, by the processing system, the historical manipulation model by further training the machine learning model using the content manipulation.   
     
     
         17 . The method of  claim 16 , wherein the characteristics of the environment further comprise a location of the user. 
     
     
         18 . The method of  claim 16 , wherein the content manipulation comprises changing a rate of presentation, presenting a still frame, presenting a visual history, repeating the presenting of the media content, or a combination thereof. 
     
     
         19 . The method of  claim 16 , wherein the content manipulation reduces a total time for the presenting of the media content. 
     
     
         20 . The method of  claim 16 , further comprising:
 receiving user feedback responsive to the presenting of the media content; and   adjusting the presenting of the media content based on the user feedback.