Distributed Service Configuration Using Risk-Adjusted User Value
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Solution Overview
Problem
Distributed computing systems fail to optimally configure service execution due to incomplete fraud and loss detection processes that do not consider user impact, leading to suboptimal user experiences and system deployments.
Innovation Solution
A system that utilizes machine learning models to generate risk and value signals, determining optimal service configurations by maximizing expected value through risk-adjusted user value calculations, considering both user experience and system risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraud and loss detection processes are performed to adjust service execution configuration, then system risk is reduced, but user experience deteriorates due to incomplete analysis of user impact factors
Solution Approach 1:
The patent segments the service execution configuration into multiple independent factors including fraud risk, loss risk, user value, and user experience metrics. Each factor is analyzed separately by dedicated machine learning models, allowing the system to optimize each dimension independently while maintaining overall balance through the expected value calculation framework.
Solution Approach 2:
The patent changes the parameter representation from simple binary fraud/loss detection to a multi-dimensional parameter space including predicted risk scores, expected user values, and configuration impact metrics. This enables nuanced adjustments to service execution configuration that balance risk reduction with user experience preservation through expected value maximization.
2Reliability
If service execution is reconfigured based on fraud or loss determinations, then system security is improved, but service deployment becomes suboptimal due to lack of comprehensive factor consideration
Solution Approach 1:
The patent performs preliminary analysis of multiple factors including user value and experience impact before finalizing service execution configuration adjustments. Machine learning models pre-calculate expected values for different configuration options, allowing the system to select optimal deployments that maintain security while avoiding suboptimal outcomes.
Solution Approach 2:
The patent implements a feedback loop where service execution results, user responses, and system performance metrics are continuously monitored and fed back into the machine learning models. This enables dynamic adjustment of service configuration to maintain optimal balance between security and deployment efficiency based on actual observed outcomes.
Data Source
AI summary
Disclosed herein are methods and systems for improved service system configuration during distributed services execution by a distributed services system. In one example, different sets of machine learning models respectively generate signals indicative of predicted risk and expected values associated with execution of a service by the distributed services system. Next, expected values associated with actions, where each action corresponds to a configuration of the service, are determined so that an action that maximizes the expected value associated with the execution of the service based on the predicted risk can be selected. The action is then executed configuring the execution of the service for a user in the distributed services system.