Distributed Messaging Trials With Dynamic Cohort Reassignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional trial procedures, such as A/B testing and multi-variate testing, lock subjects into one option for the duration of the trial, leading to potential undesirable experiences and require significant time to gather statistically relevant data, failing to account for time-varying contextual factors.
Innovation Solution
A distributed messaging platform facilitates micro-randomized trials by periodically re-assigning subjects to different cohorts at fixed intervals, allowing subjects to receive varying treatments and collect response data in real-time, enabling rapid evaluation of multiple interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional A/B testing or multi-variate testing is used to evaluate different options, then statistically relevant data can be gathered, but the trial duration becomes significantly long and subjects are locked into one option for the entire trial period
Solution Approach 1:
The patent applies dynamics by transitioning from static cohort assignment to dynamic reallocation. Subjects are periodically re-randomized into different cohorts at fixed intervals, allowing treatment assignments to change over time. This dynamic approach enables faster acquisition of statistically relevant data while preventing subjects from being permanently locked into potentially inferior options throughout the entire trial duration.
Solution Approach 2:
The patent implements periodic action through fixed-interval re-randomization of subjects into cohorts. At predetermined time points during the trial, subjects are periodically reassigned to different treatment cohorts based on updated effectiveness data. This periodic restructuring accelerates data collection for statistical significance while maintaining the flexibility to adapt treatments based on emerging results.
2Stability of the object's composition
If subjects are locked into one cohort for the duration of the trial, then cohort stability is maintained, but subjects may be locked into undesirable options and cannot benefit from potentially superior treatments
Solution Approach 1:
The patent resolves this contradiction by introducing dynamic cohort assignment that balances stability with adaptability. While cohorts maintain structural stability during each trial period, the system periodically redistributes subjects based on accumulating effectiveness data. This allows the system to adapt treatments to emerging patterns while maintaining sufficient cohort stability for meaningful measurements during each interval.
Solution Approach 2:
The patent applies feedback mechanisms by continuously monitoring treatment effectiveness data and using this information to guide subsequent cohort assignments. At fixed intervals, the system analyzes accumulated data and adjusts future cohort assignments accordingly, creating a feedback loop that adapts treatments based on real-world performance while maintaining structured trial methodology.
3Quantity of substance
If conventional trial procedures are used, then comprehensive data collection is achieved, but time-varying contextual factors are not adequately accounted for in the analysis
Solution Approach 1:
The patent addresses this contradiction through dynamic trial design that explicitly incorporates time-varying contextual factors. By periodically re-randomizing subjects and analyzing data at fixed intervals, the system captures temporal variations in treatment effectiveness and contextual factors. This dynamic approach enables comprehensive data collection while simultaneously accounting for time-dependent patterns that static trial designs miss.
Solution Approach 2:
The patent implements periodic analysis at fixed intervals to systematically account for time-varying factors. Each periodic re-randomization event and data collection cycle allows the system to measure treatment effectiveness at different time points, enabling the analysis to capture temporal patterns and contextual changes that would be overlooked in single-point-in-time conventional trials.
Data Source
AI summary
A distributed messaging platform may employ a trial system to provide a micro-randomized trial by identifying, over an electronic network, a plurality of user devices associated with a plurality of subjects; and at each of a plurality of intervals, and assigning the subjects into a respective distribution over a plurality of cohorts, such that the subjects are re-assigned at each fixed interval. The trial system may further perform operations that include, for each cohort, causing a respective subset of the user devices associated with subjects assigned to the cohort to output a respective treatment, the treatment for each cohort being different from each other; and collecting, from the user devices, response data of the subjects; such that, as the subjects are re-assigned to different cohorts across the different intervals, the user devices output different treatments corresponding to the different cohorts.