Digital Content Generation System with Feedback-Driven Model Updates
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Solution Overview
Problem
Existing digital communication systems face challenges in efficiently generating and posting tailored content due to the complexity and variety of digital communication methods, platforms, and channels, leading to resource-intensive efforts and potential noise in communication.
Innovation Solution
A system and method that utilize a processor with modules for acquiring and applying goals data to a goals application model, generating recommendations data through a recommendations model, and dynamically updating the model based on creative feedback, to produce tailored digital content and post it effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional digital communication systems are used to manage communication across multiple platforms and channels, then the reach and platforms of communication are expanded, but the resources required to generate and post tailored content increase significantly
Solution Approach 1:
The system implements a universal content generation platform that can operate across multiple digital communication channels (social media, email, SMS, instant messaging) through a single integrated AI system. The platform generates tailored content for different platforms using the same underlying technology, eliminating the need for separate resource allocation for each channel while maintaining platform-specific optimization
Solution Approach 2:
The AI system automatically generates, optimizes, and schedules content across multiple platforms without requiring manual intervention for each channel. The system self-manages the content creation process by receiving user input, generating appropriate content for different platforms, and posting it automatically, thereby reducing the resource burden on users while expanding communication reach
2Extent of automation
If conventional AI systems are used to generate communication content, then some automation is provided, but the systems lack the training and focus to determine and generate effective communication approaches
Solution Approach 1:
The system performs preliminary training and optimization of the AI model specifically for digital communication tasks before actual content generation begins. The model is pre-trained on communication-specific data and patterns, and undergoes initial optimization to understand effective communication approaches across different platforms, ensuring reliable and effective content generation from the start
Solution Approach 2:
The system implements continuous feedback loops where the AI model receives performance data from generated content across different platforms and uses this feedback to refine and improve its communication strategies. The model learns from actual communication outcomes and adjusts its approach to enhance effectiveness while maintaining automation
3Extent of automation
If general AI models are used to generate communication content, then automation is provided, but the models produce predictable or generic content such as stock photos that indicate a lack of effort
Solution Approach 1:
The system generates content with local quality optimization by tailoring the AI generation parameters and style specifically to each communication platform and context. Instead of using a single generic approach, the model adjusts its output characteristics (tone, format, visual style) to match the specific requirements of each platform while maintaining automation, thereby producing unique and effortful-looking content
4Ease of operation
If users engage with AI systems to generate communication content, then some assistance is provided, but it takes many attempts and specific training to obtain satisfactory and optimal results
Solution Approach 1:
The system performs preliminary optimization and training internally before the user actually needs content generated. The AI model is pre-trained on communication best practices and platform-specific patterns, so when the user provides input, the system can immediately generate high-quality content without requiring the user to spend time training or iterating through multiple attempts
Solution Approach 2:
The AI system autonomously handles the training and optimization processes that would otherwise require user time and effort. The model self-trains on communication data and self-optimizes its generation parameters, providing users with immediate satisfactory results without requiring their time investment in training or multiple revision attempts
Data Source
AI summary
Provided is a system and method for generating and posting tailored digital content. The system includes a processor and memory. The memory is configured to store campaign data and model data. The processor includes a goals and objectives module configured to apply goals application data to a goals application model, and generate and provide to a recommendations module goals and objectives data. The processor further includes the recommendations module configured to apply the goals and objectives data and creative feedback to a recommendations model, generate and provide to a creations module, the recommendations data including an engineered prompt, and update the recommendations model based on received creative feedback. The processor further includes an evaluation module configured to receive and evaluate the status of creative data, and where the creative data is in progress, generate and provide to the recommendations modules, the creative feedback.