Behavior Taxonomy for Habit Formation Engine
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Solution Overview
Problem
Existing life improvement applications are outcome-based and fail to help users establish positive habits effectively, as they do not focus on the behaviors necessary for habit formation and often struggle to accurately identify suitable behaviors for individual users.
Innovation Solution
A habit forming system that includes a habit forming service and app, which collects user information, determines recommended behaviors based on a user's propensity and historical data, and provides structured content for habit formation, focusing on behavior rather than outcome, using a behavior taxonomy and human behavior model to recommend effective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If life improvement applications focus on outcome-based goals, then users can set clear targets, but users fail to establish positive habits effectively
Solution Approach 1:
The patent inverts the traditional outcome-based approach by focusing on behavior-based goals instead. Rather than setting targets like 'lose 10 pounds' and measuring success by the outcome, the system sets goals around specific behaviors like 'walk 30 minutes daily' and measures success by whether the behavior was performed. This inversion shifts the focus from what users want to achieve to what actions they need to take, making habit formation more reliable while maintaining clear goal structure.
2Ease of manufacture
If recommendation engines provide generic behavior recommendations, then implementation is simple, but accuracy in identifying appropriate behaviors for individual users is poor
Solution Approach 1:
The patent applies local quality by customizing behavior recommendations to match each user's specific characteristics, preferences, and context. Instead of providing generic recommendations to all users, the system analyzes individual user data and tailors behavior suggestions to local user needs. This allows the system to maintain simple implementation through automated personalization while achieving high accuracy in behavior recommendations by adapting to each user's unique situation.
Solution Approach 2:
The system incorporates feedback loops where user responses to behavior recommendations are continuously analyzed and used to improve future recommendations. When users indicate which behaviors they're interested in or successful at performing, this feedback is fed back into the recommendation engine to refine and personalize future suggestions. This feedback mechanism enables the system to maintain simple automated operation while progressively improving recommendation accuracy for each individual user.
3Power
If users focus on achieving outcomes, then motivation is initially high, but motivational down-regulation occurs when outcomes are not achieved
Solution Approach 1:
The patent enables users to serve themselves by providing them with tools to track and measure their own behavior performance independently of outcome achievement. The system automatically monitors whether users are performing their targeted behaviors and provides self-generated feedback and recognition. This self-service approach allows users to maintain motivation through autonomous self-monitoring and self-reinforcement, preventing motivational down-regulation by making them self-sufficient in maintaining their motivational state regardless of external outcome results.
Solution Approach 2:
The system implements continuous feedback mechanisms that provide users with immediate information about their behavior performance. Rather than waiting for long-term outcomes to determine success, users receive regular feedback on whether they've completed their targeted behaviors. This frequent feedback loop maintains motivation stability by allowing users to see their progress and adjust their efforts in real-time, preventing the motivational crashes that occur when outcome-based goals are not met after extended periods.
Data Source
AI summary
Disclosed herein are system, method, and device embodiments for implementing a habit forming system. A habit forming service receives habit selection information from a habit forming application, determines a maturity measurement of a user based on a human behavior model, and determines a recommendation score of a behavior associated with the habit. Further, the habit forming service encodes the behavior onto a behavior taxonomy as a traversal path based on the maturity measurement, the recommendation score, and historic habit formation information, and identifies the behavior as a preferred behavior based on an attribute of the traversal path.