Neural Network Timing Prediction for User Communication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face inefficiencies in communicating with users due to the use of simple rule-based heuristics that do not account for individual user conditions and behavior, leading to suboptimal resource utilization and lower user response rates, as different communication channels have varying resource utilization and user response rates.
Innovation Solution
A neural network-based system is trained using user data, including communication and event time series, to predict the optimal timing for user interactions, thereby selecting the most effective communication channel and timing to maximize user response rates and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple rule-based heuristics are used to determine communication timing and mechanism, then the system is easy to implement and operate, but resource utilization is poor and user response rates are low
Solution Approach 1:
The patent replaces rule-based heuristic systems with a neural network-based machine learning system. The neural network learns optimal communication timing and channel selection from historical user interaction data, automatically adapting to individual user behaviors and patterns without requiring manual rule configuration. This substitution enables personalized, data-driven communication strategies that significantly improve resource utilization and user response rates while maintaining ease of operation through automated model training and deployment.
2Ease of operation
If simple rule-based heuristics are used to determine communication timing and mechanism, then the system is easy to implement and operate, but user response rates are low
Solution Approach 1:
The patent replaces rule-based heuristic systems with a neural network-based machine learning system. The neural network learns optimal communication timing and channel selection from historical user interaction data, automatically adapting to individual user behaviors and patterns without requiring manual rule configuration. This substitution enables personalized, data-driven communication strategies that significantly improve resource utilization and user response rates while maintaining ease of operation through automated model training and deployment.
Solution Approach 2:
The patent changes the fundamental parameters of the communication decision-making process by transitioning from static rule-based parameters to dynamic, learned parameters from neural network predictions. The system uses predicted user response probabilities and optimal timing parameters derived from training data to dynamically adjust communication strategies, enabling adaptation to changing user behaviors and improving overall response rates.
3Device complexity
If rule-based techniques are used without quantitative measures, then the system is simple to implement, but the process is difficult to adapt to continuously changing data
Solution Approach 1:
The patent replaces rule-based heuristic systems with a neural network-based machine learning system. The neural network learns optimal communication timing and channel selection from historical user interaction data, automatically adapting to individual user behaviors and patterns without requiring manual rule configuration. This substitution enables personalized, data-driven communication strategies that significantly improve resource utilization and user response rates while maintaining ease of operation through automated model training and deployment.
Solution Approach 2:
The patent introduces dynamic adaptability by implementing a neural network system that continuously learns from new user interaction data. The model can be retrained periodically or incrementally to adapt to changing user behaviors, seasonal patterns, and emerging communication preferences. This dynamic approach contrasts with static rule-based systems, enabling the organization to maintain effective communication strategies over time despite evolving user characteristics.
4Loss of energy
If incorrect communication mechanism is used to communicate with users, then communication resources are wasted, but determining the optimal mechanism requires complex analysis
Solution Approach 1:
The patent applies preliminary action by training the neural network model in advance on historical communication data to learn optimal channel selection and timing patterns for different user segments. Before actual communication campaigns, the system uses the trained model to predict the most effective communication mechanism for each target user, eliminating the need for complex real-time analysis during campaign execution. This pre-computed guidance enables efficient resource allocation and minimizes wasted communication resources.
Data Source
AI summary
A system trains a neural network for predicting time for communicating with users. The system trains the neural network using user data for users that includes a communication time series and an event time series. The system trains the neural network by masking a portion of the time series data and provides the masked time series data as input to the neural network. The system executes the neural network to predict values of the masked portion of the time series data. The system determines a loss value based on the accuracy of the prediction of the masked portion of the time series data and adjusts parameters of the neural network to minimize the loss value. The system uses the trained neural network to predict timing for communicating with a particular user.