AI Creative Layer Assembly for Personalized Ad Generation
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Solution Overview
Problem
Traditional creative generation processes are time-consuming and costly, often resulting in generic advertisements that may not resonate with individual audience members, leading to inefficiencies and lost revenue.
Innovation Solution
A Generative AI Creative Engine utilizes machine learning and AI techniques to customize advertisements based on individual audience preferences, generating tailored creative layers from owned content libraries, and allowing for real-time or pre-generated personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual creative generation processes are used, then creative content can be produced, but the process is time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical creative generation processes with an automated AI-based system. The creative engine uses machine learning models to generate creative content automatically, substituting human manual work with computational processes that operate faster and at lower cost.
Solution Approach 2:
The system enables self-service creative generation where the AI engine autonomously creates creative content without requiring manual intervention at each step. The creative engine can generate multiple variations independently, reducing dependency on human creators for every iteration.
2Adaptability or versatility
If generic advertisements are used for target audiences, then production costs are reduced, but individual audience members may not be receptive
Solution Approach 1:
The patent segments the creative content into multiple interchangeable layers (visual elements, audio elements, text elements, etc.). Each layer can be independently customized based on individual audience preferences, allowing personalized advertising without requiring complete redesign of each ad.
Solution Approach 2:
The system dynamically assembles creative content by selecting and combining different layers based on real-time audience data and preferences. This dynamic customization allows the same base creative to adapt to individual viewers without manual intervention.
3Reliability
If multiple iterations of vetting and testing are performed, then creative effectiveness is improved, but the process becomes more costly and time-consuming
Solution Approach 1:
The system performs preliminary creative generation by creating multiple candidate creatives in advance using AI. These pre-generated creatives can be quickly evaluated and selected, eliminating the need for multiple manual vetting and testing iterations.
Solution Approach 2:
The system incorporates feedback mechanisms where audience responses to generated creatives are analyzed and used to improve future creative generation. This automated feedback loop continuously improves creative effectiveness without requiring manual re-vetting.
Data Source
AI summary
Systems and methods for custom creative generation are provided herein. An electronic request to generate a custom creative from a digital content is received. Available creative layers for use in the custom creative are identified based at least in part upon one or more data tags associated with the digital content. The one or more data tags indicate creative features of the digital content. Machine learning is used to identify a subset of the available creative layers based upon generation criteria associated with the electronic request. The custom creative is generated by combining the subset of available creative layers. An electronic response is generated and provided in response to the electronic request. The electronic response includes an indication of the custom creative.