Network Safety Assessment Planning With Explainable ML Scenarios
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Solution Overview
Problem
Conventional assessment and planning technologies lack the ability to provide customized, efficient, and practical recommendations for improving assessment ratings, often leading to burdensome and ineffective action items, especially in atypical scenarios, and fail to consider specific user-defined parameters or time constraints.
Innovation Solution
A computer-implemented system using a first artificial neural network generates a weight matrix based on user-defined parameters, simulates scenarios through an analytic decision tree, and displays prioritized action items to achieve a target assessment rating within a specified time frame, providing a user-friendly interactive menu for customized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional assessment systems generate simplified assessment ratings based on many assessment factors, then quick and easy understanding of classification is achieved, but the system cannot provide clear guidance on how to improve the rating
Solution Approach 1:
The patent segments the assessment improvement process by separating the assessment rating generation from the action item generation. The system divides complex assessment factors into manageable components and provides segmented action items tailored to specific improvement goals, making the information both simplified for understanding and detailed for actionable guidance.
Solution Approach 2:
The patent introduces an intermediary planning system that acts as a mediator between the assessment rating and the user. This intermediary generates personalized action items and planning statements that translate the complex assessment factors into clear, actionable guidance, preventing information loss while maintaining ease of understanding.
2Reliability
If conventional systems provide extensive user interaction and knowledge of underlying features to suggest improvements, then comprehensive assessment is achieved, but the process becomes burdensome and confusing
Solution Approach 1:
The patent implements self-service functionality where the system automatically generates personalized action items and planning statements without requiring extensive user interaction or knowledge of underlying features. The system serves itself by using the assessment data to generate comprehensive yet user-friendly improvement guidance, maintaining reliability while improving ease of operation.
Solution Approach 2:
The patent changes the parameter of user interaction from extensive manual input to automated generation based on assessment data. By transforming the system from requiring user knowledge to automatically generating personalized plans, it maintains comprehensive assessment reliability while dramatically improving ease of operation.
3Measurement precision
If ML models are configured to give certain factors more weight than others, then predictive accuracy is improved, but it becomes unknown how the score was calculated and what may be done to improve it
Solution Approach 1:
The patent implements feedback mechanisms that provide users with personalized action items and planning statements based on the weighted assessment factors. The system feeds back specific, actionable information about how each weighted factor contributes to the overall score and what actions can improve it, preventing information loss while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary planning system that acts as a mediator between the weighted ML model and the user. This intermediary translates the complex weighted scoring into clear, actionable guidance, maintaining the precision of the weighted assessment while preventing information loss about how to improve the score.
4Adaptability or versatility
If conventional technologies require users to sift through multitudes of different options, programs, and interfaces to get suggestions, then comprehensive options are provided, but the process becomes difficult to follow and view
Solution Approach 1:
The patent applies local quality by providing personalized, localized action items and planning statements tailored to each user's specific assessment results and improvement goals. Instead of presenting all possible options universally, the system delivers locally optimized, relevant guidance that is comprehensive for the user's specific situation while being easy to follow.
Solution Approach 2:
The patent segments the comprehensive options into organized, manageable action items and planning statements. By dividing the multitude of options into structured, prioritized segments with clear instructions, the system maintains comprehensiveness while dramatically improving ease of operation and followability.
Data Source
AI summary
Computer-implemented systems, products, and methods for receiving data characterizing a set of user-define parameters from a first entity including at least a target assessment rating and a target time duration; simulating, based on the received data, one or more scenarios, wherein each of the one or more scenarios is associated with a set of action items; determining a total score for each set of action items for each scenario; removing, using an analytic decision tree, any scenario from the one or more scenarios that includes a total score that fails to satisfy a set of predetermined criteria; determining an efficiency factor associated with each of the one or more remaining scenarios; and providing the remaining one or more scenarios in a hierarchical position based on the efficiency factor.