Personalized Application Configuration With Machine Learning Feedback
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Solution Overview
Problem
Existing methods for updating mobile application parameters are time-consuming and inefficient, particularly in identifying optimal configuration values for individual users, as they often rely on manual specification and A/B testing across user segments.
Innovation Solution
A system and method utilizing machine learning to analyze user performance data and adjust application parameters on a user-by-user basis, optimizing configuration values through a machine learning algorithm that predicts user-specific settings based on past and real-time performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual specification of configuration values is used, then developers can control app parameters remotely, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically determine optimal configuration values for each user based on their behavior patterns, eliminating the need for manual developer specification. The model continuously learns from user interactions and autonomously adjusts parameters such as content recommendations, interface layouts, and feature prioritization.
Solution Approach 2:
The patent replaces the mechanical manual specification process with an automated machine learning system. Instead of developers manually defining configuration values for different user segments, the system uses algorithms that process user data, identify patterns, and generate personalized configuration recommendations automatically.
2Measurement precision
If A/B testing across user segments is used, then performance of configuration values can be evaluated, but identifying optimal values for individual users becomes tedious and infeasible
Solution Approach 1:
The system segments users into individual profiles based on their unique behavior patterns and preferences. Rather than treating users as homogeneous groups, the machine learning model creates distinct segments for each user based on their interaction history, device characteristics, and response to previous configuration changes, enabling personalized optimization.
Solution Approach 2:
The patent implements continuous feedback loops where user interactions with the app provide real-time data to the machine learning model. The model analyzes user responses to configuration changes and uses this feedback to iteratively improve its predictions, gradually converging on optimal configuration values for each individual user.
3Adaptability or versatility
If cloud-based parameter updating is used, then app features can be updated remotely without user action, but the system lacks personalization capability
Solution Approach 1:
The system transitions from static cloud-based parameter updates to dynamic, adaptive configuration. The machine learning model continuously updates its understanding of user preferences in real-time, allowing the system to dynamically adjust configuration values based on changing user behaviors, contexts, and responses, thereby achieving personalization.
Solution Approach 2:
The patent changes the approach from fixed parameter updates to adaptive parameter adjustments. The machine learning model analyzes multiple parameters including user behavior patterns, device characteristics, and contextual information to dynamically determine optimal configuration values, transforming the system from static to adaptive and enabling personalization.
Data Source
AI summary
A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.