Branching Video Element Generation With Iterative Creator Feedback
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Solution Overview
Problem
Conventional machine learning models generate predictable outputs and lack the ability to iteratively produce creative content in response to user feedback, limiting their applicability in generating diverse and innovative video elements.
Innovation Solution
A system that utilizes a machine learning model to iteratively generate branching candidate video elements by incorporating user feedback and video creator-specific information, allowing for dynamic modification of video elements such as titles, thumbnails, and scenes through a series of prompts and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are trained to generate consistent output in response to the same input, then precision and reliability are improved, but creativity and diversity of output deteriorate
Solution Approach 1:
The system dynamically adjusts the generation process by allowing iterative refinement of video elements. Users can request modifications to specific elements (titles, thumbnails, scenes) and the system generates multiple versions, enabling the model to adapt between consistency and creativity based on user feedback rather than being fixed in one mode
Solution Approach 2:
The system incorporates user feedback loops where users can review generated video elements and request modifications. This feedback mechanism allows the model to learn from user preferences and adjust subsequent generations, bridging the gap between consistent baseline performance and creative adaptability
2Manufacturing precision
If machine learning models generate predictable output based on training data, then manufacturing precision is improved, but adaptability to new creative requirements deteriorates
Solution Approach 1:
The system performs preliminary generation of video elements based on training data to establish a precise baseline, then allows iterative refinement through user feedback. This two-stage approach maintains precision from training while enabling creative adaptation through subsequent modifications
Solution Approach 2:
The generation process is made dynamic by allowing users to select specific video elements for modification and iterate through multiple versions. This transforms the static predictable output into a dynamic process that can adapt to creative requirements while maintaining precision in the underlying generation mechanism
Data Source
AI summary
Generating branching candidate video elements is disclosed, including: obtaining a first set of candidate video elements from a model by prompting the model using a first prompt including at least base data, a first modifying action, and video creator-specific information; causing the base data and the first set of candidate video elements to be presented with first branching relationships at a user interface; receiving, via the user interface, a selected candidate video element from the first set of candidate video elements and a second modifying action; obtaining a second set of candidate video elements from the model by prompting the model using a second prompt including at least the selected candidate video element, the second modifying action, and the video creator-specific information; and causing the selected candidate video element and the second set of candidate video elements to be presented with second branching relationships at the user interface.