Bayesian Evolutionary Optimization for Web Interface Design
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Solution Overview
Problem
Current methods for optimizing web interfaces, such as A/B testing and multivariate analysis, are inefficient and often lead to inaccurate results due to small sample sizes and reliance on human intuition, especially in big data environments where subtle interactions in webpage design may be missed, and existing algorithms struggle to handle the complexity of vast data inputs.
Innovation Solution
A machine learned conversion optimization (MLCO) system using evolutionary computations and Bayesian calculations to identify the most successful webpage designs by leveraging the performance data of parent candidates to calculate accurate prior probability distributions, allowing for efficient selection of winning candidates without testing all possible designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If A/B testing or multivariate analysis is used to optimize web interfaces, then human knowledge and intuition can be incorporated into design, but the sample size is small and many good candidates may never be found or implemented
Solution Approach 1:
The patent replaces traditional mechanical testing methods (A/B testing, multivariate analysis) with a genetic algorithm-based evolutionary system. Instead of manually designing and testing a limited number of webpage variants, the system automatically generates, evaluates, and evolves thousands of candidate webpages through computational simulation, substituting human-driven mechanical testing with algorithmic optimization that can process vastly more candidates efficiently
Solution Approach 2:
The patent applies preliminary action by using Bayesian calculations to estimate the performance of candidate webpages before actual deployment. The system calculates prior probabilities of conversion rates based on historical data and genetic algorithms, allowing it to predict which candidates are most likely to succeed before committing resources to full-scale testing, thus identifying promising candidates earlier in the process
2Productivity
If genetic algorithms evolve many candidate solutions, then more candidates can be tested, but small sample sizes lead to inaccurate statistical estimates and lucky responses
Solution Approach 1:
The patent implements feedback through Bayesian calculations that continuously update the probability distributions of candidate webpage performances. As more data becomes available from testing candidates, the system refines its estimates by combining prior knowledge with new observations, allowing accurate performance assessment even with limited sample sizes. This feedback mechanism prevents overinterpretation of lucky responses by constantly updating confidence intervals
Solution Approach 2:
The patent changes the parameter of estimation accuracy by using Bayesian probability distributions rather than simple point estimates. Instead of relying on small sample averages that are prone to luck, the system maintains distributions that capture uncertainty and evolves candidates based on expected performance integrated over these distributions, transforming the estimation approach from fragile to robust
3Adaptability or versatility
If conversion optimization tests multiple combinations of webpage elements, then more variations can be evaluated, but the complexity of permutations and combinations increases exponentially
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of evaluating all possible webpage combinations into smaller, manageable genetic building blocks. Instead of testing complete webpage permutations, the system segments webpage design into modular elements (headers, body content, footers, etc.) that can be independently evolved and recombined through genetic operations, reducing the complexity from exponential to linear scaling
Solution Approach 2:
The patent transitions from testing variations within a single dimension (different webpage versions) to evolving across multiple dimensions simultaneously (structural elements, content, styling, layout). The genetic algorithm operates in a high-dimensional search space where each dimension represents a design aspect, allowing the system to explore diverse combinations efficiently by moving through this expanded dimensional landscape rather than sequentially testing one dimension at a time
4Ease of manufacture
If Bayesian calculations use uniform priors for all candidates, then calculations are simpler, but they do not leverage performance data from parent candidates
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the performance data of parent candidates before generating offspring. This preliminary information is then used to inform the prior distributions of new candidates, allowing the Bayesian calculations to start with knowledgeable priors rather than uniform defaults. The system prepares the probabilistic foundation in advance, making the subsequent calculations both more accurate and efficiently targeted
Data Source
AI summary
A method for finding a best solution to a problem is provided. The method includes evolving candidate individuals in a candidate pool by testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool, and repeating the evolution steps to evolve the candidate individuals in the candidate pool. The method further includes selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.