Production Experiment System for Variant Evaluation
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Solution Overview
Problem
Existing systems fail to objectively evaluate the downstream effects of new predictive models and UI/logic variants in production environments, leading to unclear improvements in key performance indicators (KPIs) and requiring significant time and resources for testing multiple variants.
Innovation Solution
Implementing a system that performs designed experiments by selecting and evaluating UI and logic variants in production environments based on context data, allowing for the collection of synchronous and asynchronous results to prove or disprove hypotheses about variant performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If new predictive models and UI variants are tested in non-production environments with parallel predictions, then predictions can be compared directly, but the downstream effects and actual performance improvements cannot be objectively evaluated
Solution Approach 1:
The patent segments the evaluation process into distinct experimental groups within production: a control group that receives the current model and UI, and experimental groups that receive new model variants and UI variants. This segmentation allows objective comparison of downstream effects while maintaining production effectiveness, as each group's performance can be measured independently against its baseline.
Solution Approach 2:
The patent implements feedback mechanisms by collecting actual downstream results (such as claim handling metrics, customer satisfaction, processing time) from production environments and using this feedback to evaluate whether new variants outperform current versions. This closed-loop feedback enables objective measurement of real-world performance improvements rather than relying solely on predicted outcomes.
2Manufacturing precision
If multiple variants are tested manually in existing systems, then predictions can be generated and compared, but significant time and resources are required for testing
Solution Approach 1:
The patent enables the system to test variants autonomously in production environments without requiring manual intervention. The automated experiment framework selectively applies different variants to user populations, collects downstream metrics automatically, and evaluates performance objectively, thereby reducing time and resource consumption while maintaining evaluation accuracy.
Solution Approach 2:
The patent allows continuous testing of multiple variants in production environments where the system operates uninterrupted. Instead of stopping production for manual testing, the system continuously collects data on variant performance from ongoing operations, enabling parallel evaluation of multiple hypotheses simultaneously without loss of productive time.
3Ease of operation
If only current model predictions are followed in production, then implementation is simple, but the ability to learn from and improve future predictions is limited
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the system to switch between different model variants and UI versions based on experiment results. The framework enables the system to adapt its behavior dynamically - sticking with current models when performance is stable, but transitioning to improved variants when experimental data demonstrates superior downstream performance, thus balancing simplicity with adaptability.
Solution Approach 2:
The patent performs preliminary actions by conducting controlled experiments in production environments before fully implementing new models. By pre-testing and evaluating variants on subsets of users and data, the system can identify promising improvements in advance and prepare for smooth transitions, making future model updates more adaptable and less disruptive to operations.
Data Source
AI summary
Variants of an application, such as user interface variants and/or logic variants, can be used in a production environment as part of a designed experiment. An experiment manager can cause the application to operate based on different variants for different users and/or instances of input data. Synchronous and/or asynchronous results can be collected that indicate impacts of the variants at the application and/or other downstream systems. Such asynchronous and/or synchronous results can be used to prove or disprove a hypothesis associated with the designed experiment.