Trigger-Associated Messaging System With Neural Network Prioritization
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Solution Overview
Problem
Conventional messaging systems fail to effectively prioritize and deliver important messages to users with long-term health conditions, leading to potential non-adherence with therapeutic regimes and increased healthcare costs due to missed interventions.
Innovation Solution
A system that utilizes a neural network to predict candidate users for messaging based on user attributes and trigger events, prioritizing messages from users with higher influence and relevance, and schedules message delivery considering the user's non-therapeutic activities to maximize adherence to therapeutic regimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional messaging systems present messages in date order, then all messages are delivered to users, but important messages can be missed and user adherence to therapeutic regimes deteriorates
Solution Approach 1:
The system changes the parameter of message presentation from chronological order to priority-based order. A neural network analyzes user attributes, message characteristics, and contextual factors to assign priority scores, transforming how messages are ranked and displayed to users.
Solution Approach 2:
The patent replaces the simple mechanical sorting mechanism (date-based ordering) with an intelligent neural network system that processes multiple inputs and generates priority rankings, substituting basic automation with advanced AI-based decision making.
2Measurement precision
If a neural network is trained with multiple stages and feedback, then prediction accuracy for candidate users improves, but system complexity and training time increase
Solution Approach 1:
The training process is segmented into multiple stages: initial training with basic user attributes, then fine-tuning with feedback from message interactions and adherence outcomes. This divides the complex training task into manageable phases, each improving specific aspects of prediction accuracy.
Solution Approach 2:
The system implements feedback loops where actual message engagement data and adherence outcomes are fed back into the neural network to continuously improve predictions. This uses real-world results to refine the model, enhancing accuracy while managing complexity through iterative improvement.
3Reliability
If message delivery is optimized based on user activities and timing, then adherence to therapeutic regimes improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary analysis of user patterns, activity schedules, and historical engagement data to pre-determine optimal message delivery times. By anticipating when users are most receptive based on their routines, the system reduces the need for complex real-time decision making during actual message delivery.
Data Source
AI summary
In certain embodiments, trigger-associated user messaging may be facilitated. In some embodiments, a first set of candidate users for messaging with a first user in connection with occurrence of a first trigger associated with the first user may be obtained. The first user may be monitored via one or more sensors for occurrence of the first trigger. A first occurrence of the first trigger may be determined based on the monitoring. Based on the first occurrence of the first trigger, the first set of candidate users may be accessed to initiate messaging between the first user and one or more users of the first set of candidate users. Messaging between the first user and a second user (of the first set of candidate users) may be initiated based on the second user having a higher priority than a third user of the first set of candidate users.