Fragmented Generative Content Editing for Brand-Voice Alignment
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Solution Overview
Problem
Existing generative models, such as LLMs, struggle to generate content that aligns with a brand's personality and voice, requiring extensive iterative prompting and resource consumption, leading to inefficiencies and resource waste.
Innovation Solution
A content generation system using a model set with fragment-specific generative models and a defined execution order, allowing for initial content generation and interactive editing by freezing and unfreezing fragments, reducing the need for extensive back-and-forth iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing generative models (LLMs) are used to generate content, then content can be produced, but the content fails to align with brand personality and voice, requiring extensive iterative prompting and resource consumption
Solution Approach 1:
The patent segments the content generation process into multiple specialized generative models, each trained to generate specific content types (e.g., headlines, body copy, calls-to-action). This segmentation allows each model to specialize in maintaining brand voice for its specific content type, improving alignment precision while reducing the computational resources needed compared to using a single general-purpose LLM for all content generation.
Solution Approach 2:
The patent applies local quality by training each generative model on specific content types with tailored instructions and brand guidelines. Each model develops specialized knowledge for its content type, ensuring high-quality brand alignment locally for each content segment rather than relying on a general model's broad but shallow understanding.
2Manufacturing precision
If extensive iterative prompting is used with existing generative models, then content alignment improves, but productivity decreases due to time-consuming back-and-forth iterations
Solution Approach 1:
The patent performs preliminary action by pre-training multiple generative models on specific content types with brand voice guidelines before actual content generation. This upfront preparation embeds brand alignment knowledge into each model, eliminating the need for extensive iterative prompting during production and enabling high-speed, high-quality content generation in a single pass.
3Device complexity
If a single generative model is used for all content types, then device complexity is reduced, but the ability to generate aligned content across different content types deteriorates
Solution Approach 1:
The patent segments the content generation system into multiple specialized models, each dedicated to a specific content type. This segmentation improves alignment precision for each content type while managing complexity through modular architecture, where each model can be independently trained, maintained, and optimized.
Solution Approach 2:
The patent achieves universality by creating a family of generative models that share common training methodologies, architecture patterns, and brand guideline frameworks. While each model specializes in a specific content type, they collectively provide universal content generation capability across all content types, maintaining consistency in brand voice throughout.
Data Source
AI summary
Some aspects relate to technologies for employing generative models for content generation and interactive content editing. In accordance with some aspects, user input is received for generating a content item of a content type having a number of fragments. A model set for the content type is identified. The model set comprises generative models for the fragments and an execution order specifying an order for generating the fragments. A root generative model from the model set is caused to generate text for a root fragment in the execution order based on the user input. Each subsequent generative model in the model set is sequentially caused to generate text for each subsequent fragment in the execution order for the model set, wherein input for each subsequent generative model includes text of any previous fragments in the execution order. The content item is generated by combining the text of the fragments.