ML Communication Platform Optimizing User Adherence
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Solution Overview
Problem
Organizations face inefficiencies in communicating with users due to inadequate resource utilization and lack of personalized approaches, leading to suboptimal user response rates and wasted resources, as existing rule-based heuristics fail to adapt to changing user conditions and behaviors.
Innovation Solution
A machine learning-based system that receives user profile data to predict adherence rates, ranks users, and determines optimal communication channels and timing for personalized interactions, maximizing user response while minimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rule-based heuristics are used to determine communication methods, then the system is simple to implement, but resource utilization is inefficient and user response rates are low
Solution Approach 1:
The patent replaces rule-based heuristics (mechanical system) with machine learning models that analyze user profile data, historical behavior, and contextual factors to dynamically determine optimal communication methods. This substitution enables adaptive decision-making that improves resource utilization while maintaining system operability through automated model inference.
Solution Approach 2:
The system changes the parameters used for communication decisions from fixed rules to dynamic variables including user adherence rates, predicted response probabilities, and real-time contextual factors. These parameter changes allow the system to optimize communication resource allocation based on individual user characteristics and changing conditions.
2Ease of operation
If rule-based heuristics with broad user categorizations are used, then the system is easy to operate, but it cannot adapt to continuously changing user data and conditions
Solution Approach 1:
The patent implements dynamic adaptability by using machine learning models that continuously process new user profile data, historical interactions, and changing contextual factors. The system adapts to individually evolving user preferences and behaviors without requiring manual rule updates, maintaining ease of operation through automated learning and adjustment.
Solution Approach 2:
The system incorporates feedback loops where user responses to communications are fed back into the machine learning models to continuously improve prediction accuracy. This feedback mechanism enables the system to adapt to changing user conditions and preferences over time while maintaining operational simplicity through automated model retraining and adjustment.
3Loss of energy
If incorrect communication mechanisms are used to communicate with users, then resource consumption is reduced, but user response rates decrease and organizational goals are not achieved
Solution Approach 1:
The system dynamically changes communication parameters including channel selection, timing, and messaging content based on machine learning predictions of user preferences and optimal response conditions. By optimizing these parameters for each user, the system maximizes response rates while minimizing resource consumption through targeted rather than blanket communications.
Solution Approach 2:
The patent applies local quality optimization by tailoring communication mechanisms to individual user characteristics, historical behaviors, and predicted preferences rather than using uniform approaches. This personalized approach ensures that each communication is optimized for maximum effectiveness, improving response rates while reducing wasted resources on inappropriate communication methods.
4Quantity of substance
If the organization attempts to communicate with all eligible users, then user coverage is maximized, but communication resources are insufficient and time constraints are violated
Solution Approach 1:
The system applies partial action by using machine learning models to identify and prioritize a subset of users most likely to respond positively to communications. Rather than attempting to reach all eligible users, the system focuses resources on high-probability targets, achieving effective coverage of responsive users within time constraints while potentially excluding low-probability cases.
Solution Approach 2:
The system changes the parameter of user selection from inclusive (all eligible users) to selective (high-probability respondents) based on machine learning predictions. This parameter change enables the organization to maximize impact within resource and time constraints by communicating with the optimal subset of users rather than attempting universal coverage.
Data Source
AI summary
A system according to an embodiment optimizes communications with users using machine learning based models. The system receives user profile data for a set of users. For each user from the set of users, the system provides the user profile data as input to a machine learning based model and determines attributes describing the user, for example a measure of adherence rate for the user. The system ranks the set of users based on the predicted attributes. The system selects a subset of users from the set of users based on the ranking. For each selected user from the set of selected users, the system determines communication parameters for communicating with the selected user and sends a communication to the selected user based on the determined communication parameters.