AI Notification System Using Response Time Analysis for Intervention Timing
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Solution Overview
Problem
Artificial intelligence systems face challenges in anticipating undesirable user actions due to a lack of high-quality training data and limited context, leading to ineffective interventions that may inconvenience users.
Innovation Solution
Training AI models on user response times and notification characteristics to determine opportune moments for intervention, generating time-sensitive notifications that minimize user inconvenience and reduce undesirable actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are trained on large amounts of high-quality data to improve prediction accuracy, then the ability to anticipate undesirable actions improves, but the complexity and time required to obtain and process this data increases significantly
Solution Approach 1:
The patent extracts only the most relevant features from user data (notification interactions, response times, action patterns) rather than processing all available data. This selective extraction maintains prediction accuracy while significantly reducing data processing complexity and resource requirements.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns during normal operation, continuously learning from notification interactions and response times. This ongoing preliminary action prepares the model for accurate predictions without requiring complex batch processing of large datasets.
2Reliability
If AI systems intervene early to prevent undesirable actions, then the effectiveness of intervention improves, but the risk of causing user inconvenience increases
Solution Approach 1:
The patent applies different intervention strategies based on local characteristics of each user and situation. The system analyzes individual user patterns (response times, notification preferences, action histories) to customize intervention timing and messaging, ensuring high effectiveness while minimizing inconvenience for each specific user context.
Solution Approach 2:
The system dynamically adjusts intervention parameters (timing, notification type, messaging tone) based on real-time analysis of user behavior patterns. By changing these parameters according to user response times and interaction histories, the system optimizes intervention effectiveness while reducing perceived inconvenience.
3Loss of information
If the system generates notifications for all detected actions, then comprehensive monitoring is achieved, but user engagement decreases due to notification fatigue
Solution Approach 1:
The patent implements partial action by selectively generating notifications only for high-priority actions based on user patterns and risk assessment. Rather than notifying about all detected actions, the system identifies and responds to only those actions that warrant user attention, maintaining monitoring completeness for all actions while reducing notification volume to preserve engagement.
Solution Approach 2:
The system continuously monitors user responses to notifications and uses this feedback to refine its notification strategy. By analyzing engagement patterns and user reactions, the system learns which notifications are necessary and which can be omitted, optimizing the balance between monitoring completeness and user engagement over time.
Data Source
AI summary
Methods and systems are described herein for novel uses and/or improvements to artificial intelligence applications. As one example, methods and systems are described herein for enabling a resource management communications platform (e.g., an artificial-intelligence-based chatbot application) to intervene at opportune moments to reduce the likelihood that a user performs an undesirable action, while also minimizing the inconvenience to the user. For example, the system may identify implicit user information based on previous notifications and the characteristics of those notifications. Characteristics of previous notifications may include response times from the user.