Dynamic Graphical Ad Layout Generation for User Devices
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Solution Overview
Problem
Existing systems for computerized graphical advertisement displays generate static layouts that are not tailored to specific user devices, applications, or users, leading to suboptimal ad performance.
Innovation Solution
A method to dynamically generate graphical display source code based on computer-generated layout descriptions, considering user device capabilities, behavioral data, and contextual factors to optimize layout characteristics such as positional information, product arrangement, and promotional elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static layouts are used for computerized graphical advertisement displays, then the layout structure is simple and easy to implement, but the adaptability to different user devices, applications, and users is poor
Solution Approach 1:
The patent implements dynamic layout generation by creating multiple candidate layouts with different characteristics (e.g., grid-based, list-based, masonry) and selecting the most appropriate layout based on real-time analysis of user device capabilities, application context, and user behavioral data. This transforms the static layout approach into a dynamic system that adapts to varying conditions while maintaining manageable complexity through automated selection algorithms.
Solution Approach 2:
The system changes layout parameters such as grid density, column count, item size, and arrangement patterns based on device screen dimensions, resolution, and user preferences. By varying these parameters dynamically, the system achieves high adaptability across different user devices and contexts without requiring completely different layout templates for each scenario.
2Reliability
If static layouts are used for advertisement displays, then the system complexity is low, but the interaction probability and sales potential are reduced due to lack of personalization
Solution Approach 1:
The system incorporates feedback loops that analyze user interactions with advertisement displays and use this information to refine layout selections. By monitoring behavioral data such as click patterns, viewing time, and conversion rates, the system continuously improves its ability to select high-performing layouts, thereby increasing reliability of ad performance while justifying the increased system complexity through measurable performance gains.
Solution Approach 2:
The system performs preliminary analysis of user device capabilities, application context, and user behavioral data before generating or selecting layouts. This advance preparation allows the system to pre-determine the most effective layout configuration for each specific scenario, ensuring high ad performance from the outset without requiring complex real-time adjustments during display.
3Productivity
If dynamic layout generation based on user data and context is implemented, then the adaptability and performance are improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the layout generation process into distinct modular components: device capability analysis, contextual factor evaluation, candidate layout generation, scoring and selection, and rendering. Each module handles a specific aspect of the optimization process independently, which reduces computational complexity by avoiding monolithic processing and enables parallel execution of independent analysis tasks.
Solution Approach 2:
The system generates a limited set of candidate layouts (e.g., top 5-10 candidates) rather than exhaustively evaluating all possible layout configurations. By focusing computational resources on generating and evaluating only the most promising candidates based on preliminary filtering, the system achieves high productivity without requiring excessive computational processing power.
Data Source
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AI summary
A computer receives a request for graphical display source code for a computerized graphical advertisement display. The computer retrieves one or more seed layout descriptions, each seed layout description specifying associated constrained layout characteristics, and generates candidate layout descriptions based on at least the seed layout descriptions, each candidate layout description specifying associated constrained layout characteristics, where one or more of the candidate layout descriptions are generated by the computer altering one or more associated constrained layout characteristics of a seed layout description. The computer generates layout scores, each layout score associated with a candidate layout description. The computer selects a selected layout description from the candidate layout descriptions based on the layout scores. The computer generates the graphical display source code based on the selected layout description, a size of the computerized graphical advertisement display, and display capabilities of a user device.