Smart Entity Cloning for Ad Customization
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Solution Overview
Problem
Manually creating multiple versions of an electronic media entity, such as advertisements, is a tedious and inefficient process that scales poorly, especially when targeting different demographics or adjusting for different device categories.
Innovation Solution
A system and method for smart entity cloning using a large language model (LLM) to efficiently generate modified versions of text content associated with entities, such as advertisements, by receiving text content, generating prompts for the LLM, executing the LLM to create modified text content, and storing the new content as a data object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of an entity are manually created to target different demographics or device categories, then the entity can be customized for specific audiences, but the process becomes tedious and does not scale
Solution Approach 1:
The system creates templates from existing successful entities that can be copied and adapted for different demographics and device categories. Instead of manually creating each version from scratch, the system replicates proven entities with automated modifications based on target audience parameters.
Solution Approach 2:
The system automatically modifies entity parameters (such as text content, images, and configuration settings) based on target demographic and device category specifications. By changing key parameters of a base template, the system generates customized versions for different audiences without requiring manual recreation of each element.
2Adaptability or versatility
If an entity includes dozens or hundreds of individual elements, then the entity can be highly detailed and comprehensive, but manually creating multiple versions becomes increasingly tedious
Solution Approach 1:
The system segments the entity into modular components (text elements, images, configuration settings) that can be independently modified. Each element is treated as a separate template variable that can be automatically adjusted based on target demographics, making it easier to manage and replicate complex entities with many individual elements.
Solution Approach 2:
The system creates universal templates that serve multiple purposes across different demographics and device categories. A single base template with parameterized elements can generate numerous specialized versions, reducing the need to manually create and maintain separate detailed versions for each target audience.
3Productivity
If manual processes are used to create entity versions, then control over each element can be maintained, but the process scales poorly
Solution Approach 1:
The system performs automatic modifications to entity templates based on specified target demographics and device categories. Once the template structure is defined, the system self-services by automatically generating customized versions without requiring manual intervention for each element, thereby enabling scaling while maintaining consistent control over the modification process through parameter specifications.
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: (i) receiving, by a processor, text content from an entity that stores the text content as a data object associated with the entity, (ii) generating, by the processor, a prompt for a large language model that comprises the text content and directions for modifying the text content, (iii) providing, by the processor, the prompt to the large language model, (iv) executing, by the processor, the large language model, the execution causing creation of modified text content in accordance with the directions for modifying the text content from the prompt; (v) receiving, by the processor from the large language model, the modified text content, and (vi) creating, by the processor, a new data object that stores the modified text content in association with the entity.