Causal Inference Model for Unhealthy Behavior Trigger Identification
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Solution Overview
Problem
Existing systems for behavioral tracking and identification fail to analyze data in behavioral aspects, lacking the ability to preempt unhealthy habits such as smoking and alcohol consumption, which are often influenced by environmental contexts.
Innovation Solution
A processor-implemented method and system that fetches context and health behavior time-series data, determines correlations, generates a causal inference model, and provides nudges to users identified as being at risk of engaging in unhealthy behaviors by processing context-specific information using the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing systems track behavioral data, then data collection capability is improved, but ability to analyze behavioral aspects and preempt unhealthy habits deteriorates
Solution Approach 1:
The patent segments the analysis process into distinct modules: context data processing module, health behavior data processing module, correlation analysis module, and causal inference module. Each module handles specific aspects of the data, enabling comprehensive behavioral analysis while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces correlation analysis and causal inference models as intermediary processing layers between raw data collection and health outcome prediction. These intermediaries transform raw context and behavior data into meaningful insights about unhealthy habit triggers, bridging the gap between data quantity and analytical depth.
2Measurement precision
If systems monitor health parameters continuously, then health monitoring accuracy is improved, but ability to identify environmental triggers and provide preventive intervention deteriorates
Solution Approach 1:
The patent performs preliminary correlation analysis between context data and health behavior data to identify potential unhealthy habit triggers before they manifest as actual health problems. By detecting patterns and relationships in advance, the system enables preventive interventions rather than reactive responses.
Solution Approach 2:
The patent establishes a feedback loop where monitored health parameters and context information continuously inform the causal inference model, which generates predictions about potential unhealthy behaviors. This feedback mechanism allows real-time adaptive monitoring and timely preventive nudges based on evolving patterns.
Data Source
AI summary
Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to pre-empt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.