Dynamic Content Transformation via ML Models
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Solution Overview
Problem
Conventional digital media content delivery is inflexible, as it is pre-rendered and fixed in terms of visual and audio features, failing to accommodate the diverse preferences of individual users, leading to a less engaging experience despite high production values.
Innovation Solution
The implementation of user-responsive dynamic content transformation systems utilizing trained machine learning models to transform pre-rendered content components in real-time based on user behavior, gestures, environmental factors, and IoT interactions, allowing for automated or human-controlled modifications of audio and video features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If media content is pre-rendered and fixed in terms of visual and audio features, then production quality and consistency are maintained, but the content cannot accommodate diverse user preferences and becomes less engaging
Solution Approach 1:
The patent segments media content into multiple distinct components (e.g., visual effects layers, audio tracks with different characteristics) that can be independently selected and combined. This allows the system to offer personalized content configurations without requiring complete re-rendering, thus improving adaptability while managing complexity through modular content structure.
Solution Approach 2:
The system dynamically transforms pre-rendered content components in real-time based on user preferences and behavior. Rather than static pre-rendering, the content delivery system adapts visual and audio features dynamically, resolving the contradiction between maintaining production quality and accommodating user diversity through real-time customization.
2Ease of operation
If content features are dynamically transformed in real-time based on user preferences, then user engagement and satisfaction are enhanced, but processing resources and system complexity increase
Solution Approach 1:
The system performs preliminary rendering of multiple content components and variations in advance, storing them for later retrieval and combination. This pre-computation approach reduces real-time processing energy consumption while still enabling dynamic customization based on user preferences, as the heavy computational work is done beforehand when energy resources are more abundant.
Solution Approach 2:
The patent applies dynamic transformation selectively to specific content components (e.g., only transforming audio tracks or only adjusting visual effects) rather than processing the entire content uniformly. This localized approach reduces overall processing energy consumption while still providing personalized user experience in the most impactful areas.
3Adaptability or versatility
If multiple versions of content are created to accommodate different user preferences, then content versatility improves, but production resources and time consumption increase
Solution Approach 1:
Instead of creating completely separate content versions, the patent segments content into reusable components that can be independently modified and recombined. This segmentation allows a single production process to generate multiple adaptable content configurations, improving content versatility without proportionally increasing production resources and time.
Solution Approach 2:
The system creates content components with universal applicability that can serve multiple user preference scenarios. A single set of pre-rendered components can be combined in different ways to satisfy diverse user preferences, eliminating the need to produce entirely separate content versions for each user type and thus maintaining production efficiency while achieving high adaptability.
Data Source
AI summary
A system includes a hardware processor and a memory storing software code and one or more machine learning (ML) model(s) trained to transform content. The hardware processor executes the software code to ingest content components each corresponding respectively to a different feature of multiple features included in a content file, receive sensor data describing at least one of an action or an environment of a system user, and identify, using the sensor data, at least one of the content components as content to be transformed. The hardware processor further executes the software code to transform, using the ML model(s), that identified content to provide at least one transformed content component, combine a subset of the ingested content components with the at least one transformed content component to produce a dynamically transformed content, and output the dynamically transformed content in real-time with respect to ingesting the content components.