Behavior Intervention AI Using Biometric Reaction Prediction
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Solution Overview
Problem
Existing behavior modification approaches, particularly in fields like military and business, are reactive and rely on past data, lacking flexibility to respond to unforeseen events and failing to accurately represent individual thought and behavior patterns.
Innovation Solution
A system that analyzes biometric information, such as brain patterns, using machine learning to predict future behavior and design interventions, employing Thought Reproduction AI, Behavior Prediction AI, and Behavior Modification AI to tailor interventions for each individual.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing behavior modification approaches rely on past data and reactive methods, then they can be implemented with simple systems, but they lack flexibility to respond to unforeseen events and fail to accurately represent individual thought and behavior patterns
Solution Approach 1:
The system performs preliminary actions by machine-learning thought and action patterns based on past words and actions before predictions are needed. This pre-processing of individual behavioral data enables the system to respond flexibly to unforeseen events while maintaining manageable complexity through advance preparation of predictive models
Solution Approach 2:
The system segments the complex task of behavior prediction into distinct functional modules: a first device for storing biometric information, a second device for machine-learning patterns, a third device for predicting words and actions, and a fourth device for designing interventions. This segmentation allows each component to handle specific aspects of the problem, improving adaptability while controlling overall system complexity
2Measurement precision
If existing approaches use general behavioral models, then they are easier to implement, but they fail to accurately represent individual thought and behavior patterns
Solution Approach 1:
The system applies local quality by creating customized predictive models for each target person based on their specific biometric information, past words, and actions. Instead of using a uniform general model, the system tailors the machine-learning process to capture individual thought and behavior patterns, thereby achieving high measurement precision while managing complexity through focused individualized analysis
3Measurement precision
If the system analyzes detailed biometric information such as brain patterns, then prediction accuracy improves, but information processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential features and patterns from detailed biometric information such as brain patterns that are relevant for predicting words and actions. By selectively extracting meaningful patterns rather than processing all raw biometric data, the system achieves high prediction accuracy while reducing information processing complexity and managing system resources effectively
Data Source
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AI summary
A system (300) for designing intervention(s) to introduce a target person (172) to modify his/her actions, includes a first device (132) for storing biometric information about the target person (172), the biometric information including reactions having been made by the target person (172) in response to past external stimulation directed to the target person (172), a second device (131A) for recreating thought/action pattern of the target person (172) by means of prediction model (210) having been machine-learned using the biometric information as teacher data (200), a third device (131B) receiving present external stimulation and/or expected future external simulation directed to the target person (172), and outputting expected reactions of the target person (172) to be made in response to those external stimulation, and a fourth device (131C) for designing intervention(s) to introduce the target person (172) to take target action(s) in dependence on the expected reactions.