Behavioral Pattern Detection for Adaptive Health Notifications
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Solution Overview
Problem
Current digital health technologies lack effective methods for communicating activity-related notifications that adapt to user behavioral patterns, leading to inefficient habit formation and maintenance.
Innovation Solution
A method that detects behavioral patterns over a period, identifies deviations, and delivers tailored notifications through a mobile computing device, using a combination of wearable devices and environmental data to arm recommendations and prompts based on pattern strength and deviation direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If activity-related notifications are delivered using current digital health technologies, then users receive generic activity reminders, but the notifications do not adapt to user behavioral patterns leading to inefficient habit formation
Solution Approach 1:
The system continuously monitors user activity data and provides feedback by comparing actual behavior against predicted behavioral patterns. When deviations are detected, the system adjusts notification timing and content based on this feedback loop, enabling dynamic adaptation to individual user patterns and improving habit formation efficiency
Solution Approach 2:
The notification system transitions from static, pre-programmed schedules to dynamic, adaptive timing based on real-time analysis of user behavior. The system continuously learns from user patterns and adjusts notification delivery timing, making the system flexible and responsive to individual user needs rather than following fixed protocols
2Ease of operation
If behavioral pattern detection is implemented using health monitor data, then timely and relevant notifications can be delivered, but the system complexity increases
Solution Approach 1:
The patent introduces a behavioral pattern detection module as an intermediary layer between raw health monitor data and the notification system. This module processes and interprets sensor data to identify behavioral patterns, thereby simplifying the overall system architecture by centralizing the analytical function and enabling effective adaptive notifications without requiring complex integration across all system components
3Measurement precision
If health monitor data is used to detect behavioral patterns, then individual user behaviors can be identified, but data processing requirements and computational load increase
Solution Approach 1:
The system applies partial action by focusing computational resources on detecting specific, pre-defined behavioral patterns rather than analyzing all possible data dimensions. The behavioral pattern detection module prioritizes identifying key habit-related behaviors, processing only the necessary subset of health monitor data required for accurate pattern recognition, thereby reducing overall computational load and energy consumption while maintaining detection accuracy
Data Source
AI summary
A method for communicating activity-related notifications to a user includes: receiving a record of activity events of a particular activity type performed by the user over a period of time; selecting a first time-based filter from a set of time-based filters; identifying a cluster of activity events in the record of activity events filtered according to the first time-based filter; identifying an early bound and a late bound on start times of activity events of the particular activity type from the cluster; communicating a notification of a first type to the user at a first time within a threshold time of the early bound on a day fulfilling the first time-based filter; and communicating a notification of a second type to the user at a second time within a threshold time of the late bound on a day fulfilling the first time-based filter.