Temporal Path Neural Network for Educational Journey Modeling
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Solution Overview
Problem
Existing journey decision-making systems are inefficient and inflexible, often relying on general statistics and rules, failing to provide a comprehensive 'inception to benefits realization' analysis, particularly in temporal path decisions like career and education planning.
Innovation Solution
A platform using neural networks to segment, model, and design educational journeys based on statistical correlations, allowing users to input grades and personalities to determine optimal career paths with probabilistic attributes, enabling 'what-if' analysis and re-determination of paths with additional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional journey decision-making systems use general statistics and rules, then the system is simple to implement, but the system lacks flexibility and comprehensiveness in providing 'inception to benefits realization' analysis
Solution Approach 1:
The patent segments the journey decision-making process into multiple temporal layers (past, present, future events) and dimensions (user-specific, organization-specific, market-specific factors). This segmentation allows the system to comprehensively analyze journeys from inception to benefits realization while maintaining manageable complexity through structured decomposition of the analysis framework.
Solution Approach 2:
The patent introduces multiple analytical dimensions beyond traditional single-path analysis, including temporal dimensions (different time points), probabilistic dimensions (multiple possible outcomes with probabilities), and multi-stakeholder dimensions (student, parent, school, employer perspectives). This dimensional expansion enables comprehensive 'inception to benefits realization' analysis while the systematic approach manages the increased complexity.
2Adaptability or versatility
If existing systems provide restrictive and inflexible journey mapping, then the system is easier to control, but the system overlooks certain subsets of options and lacks adaptability
Solution Approach 1:
The patent implements dynamic journey mapping where the system adapts to different user needs, preferences, and circumstances. The neural network dynamically adjusts the analysis based on user-specific factors, organization-specific factors, and market-specific factors, allowing the system to be flexible and comprehensive while maintaining systematic control through the structured neural network framework.
Solution Approach 2:
The patent changes key parameters of the journey analysis including probabilistic outcomes, temporal sequencing of events, and multi-criteria evaluation. By varying these parameters based on user input and contextual factors, the system becomes highly adaptable to different scenarios while the underlying neural network structure maintains operational control and consistency.
3Reliability
If traditional systems rely on single use cases and general anecdotes, then the system is simpler to implement, but the system is inefficient and may provide incorrect journey mapping
Solution Approach 1:
The patent incorporates feedback mechanisms where the neural network learns from training data and continuously improves its journey mapping accuracy. The system uses feedback from user interactions, outcome data, and contextual factors to refine its predictions and recommendations, ensuring reliable and accurate journey mapping while the automated feedback loops manage the complexity of the analysis framework.
Solution Approach 2:
The patent replaces traditional mechanical rule-based systems with a neural network-based intelligent system. This substitution enables the system to process complex multi-factor analysis, temporal sequencing, and probabilistic outcomes automatically, achieving high reliability and accuracy in journey mapping while the neural network's learned patterns manage the complexity that would be impossible to handle with manual rule-based approaches.
Data Source
AI summary
A platform that integrates and collates the data points from students, employers, schools, and industry into an ecosystem which allows for customers (students, employers, schools, and industry) to model ‘what-if’ scenarios based on their industry parameters. By using a design algorithm based on automated reasoning, game theory, and knowledge mining, within a neural network, the platform can predict, model, and build the journey. The decision modeling neural learning platform may be used to augment or replace the need for guidance counselors in schools, along with assisting industry and immigration liaisons.