Entertainment Media Element Modification via Neural Networks
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Solution Overview
Problem
Conventional systems fail to effectively and undetectably remove objectionable content from entertainment media, potentially disrupting the viewing experience by skipping or blocking content, which can be detectable and may cause users to miss other important elements.
Innovation Solution
A computing controller analyzes and modifies individual elements of entertainment media using techniques like generative neural networks, autoencoders, and GANs to transform content from a first character to a second character that aligns with user preferences, allowing for real-time modification of graphic, language, or tone without altering the overall media's integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional systems skip or block objectionable content, then objectionable material is removed, but the viewing experience is disrupted and users may miss important elements
Solution Approach 1:
The system extracts and removes only the specific objectionable elements (such as graphic violence, profanity, or inappropriate language) from the media content while leaving the rest of the content intact. This allows objectionable material to be removed without disrupting the overall viewing experience or causing users to miss important narrative elements.
Solution Approach 2:
The system applies different processing qualities to different parts of the content - objectionable elements are modified or removed while non-objectionable elements remain unchanged. This localized approach ensures that only harmful factors are affected, preserving the integrity and coherence of the overall viewing experience.
2Object-affected harmful factors
If parental controls block objectionable material, then children are protected from inappropriate content, but the content delivery becomes rigid and may miss contextual nuances
Solution Approach 1:
The system dynamically adjusts content delivery based on user preferences, device type, and contextual factors. Rather than applying rigid blocking rules, the system can adaptively modify content - for example, blurring graphic content instead of completely blocking it, or providing content warnings - allowing both child protection and contextual nuance preservation.
Solution Approach 2:
The system changes parameters of the content delivery such as visual filters, audio muting, or content substitution based on the specific objectionable element detected. This allows flexible adaptation to different parental control requirements while maintaining content coherence and contextual understanding.
3Ease of operation
If content is modified to align with user preferences, then user satisfaction increases, but the original artistic intent may be altered
Solution Approach 1:
The system extracts and removes only the specific elements that conflict with user preferences (such as graphic violence or profanity) while preserving the rest of the original content. This minimizes loss of original artistic intent by affecting only the problematic portions rather than altering the overall work.
Solution Approach 2:
The system applies modification only to local portions of the content that require adjustment for user preferences, leaving the majority of the original content untouched. This localized approach maintains the original artistic intent and narrative coherence while still accommodating user needs.
Data Source
AI summary
Content of entertainment media that is being consumed by a user is analyzed. An element of the content that is of a first character is identified. A preference associated with the user to consume entertainment media that contains elements of a second character is identified. An updated version of the element is generated. The updated version of the element is of the second character, such that the media is consumed by the user with the element in the updated version.