Conversational Content Creation Interface Using LLMs to Reduce Manual Fields
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Solution Overview
Problem
Existing content creation systems require manual input into numerous structured fields, which is inefficient and prone to user error, especially for tasks like generating advertisements where multiple inputs are needed.
Innovation Solution
Implementing machine-learned models, trained using knowledge distillation and tuned with prompts, to analyze user input and generate content item components such as headlines and descriptions, reducing the need for manual input and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual input into structured fields is used for content creation, then content can be generated with structured control, but the process becomes inefficient and prone to user error
Solution Approach 1:
The patent replaces the mechanical manual input system with an AI-based automated content generation system. The AI model processes natural language or minimal inputs to automatically generate structured content components (headlines, descriptions, CTAs), eliminating the need for users to manually fill multiple structured fields and reducing both time consumption and error rates
Solution Approach 2:
The system enables self-service content creation where the AI model autonomously generates content components based on minimal user input. The model independently performs tasks such as generating headlines, descriptions, and call-to-action buttons without requiring user intervention in each step, thereby improving efficiency while maintaining structured output quality
2Manufacturing precision
If multiple structured input fields are required for content generation, then content quality can be controlled, but the number of interface screens and processing steps increases
Solution Approach 1:
The patent extracts the complexity of content generation from the user interface by implementing an AI model that handles content creation autonomously. The system maintains quality control through structured output formats and validation mechanisms while removing the need for users to navigate multiple interface screens and fill numerous input fields
Solution Approach 2:
The AI model serves multiple functions within a single interface: it generates headlines, descriptions, call-to-action buttons, and other content components simultaneously. This multi-functional approach maintains comprehensive content quality control while simplifying the user interface to a single interaction point
3Productivity
If automated content generation is implemented using machine learned models, then user error decreases and efficiency improves, but model training and evaluation complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the AI model on extensive content data before deployment. The model undergoes training, evaluation, and threshold setting in advance, so that during actual content generation, it can operate autonomously and efficiently without requiring complex real-time processing or user management of training complexity
Data Source
AI summary
Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.