Dynamic Website Access Control via Machine Learning Safety Ranks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for controlling children's access to online content are cumbersome, time-consuming, and require significant knowledge, as they rely on manual white and black lists that are difficult to maintain, especially given the dynamic nature of websites and varying appropriateness based on age and time.
Innovation Solution
A system that uses crowd-sourced data and machine-learning techniques to rank websites based on safety, combining user profiles with website-specific information to create dynamic access rules, allowing or denying access through a router, and enabling remote administration for real-time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual white lists and black lists are used to control website access, then access control functionality is provided, but the system becomes cumbersome and time-consuming to maintain
Solution Approach 1:
The system enables self-service through automated safety rank assignment and dynamic access rule generation. The machine learning model automatically evaluates websites and assigns safety ranks without requiring manual intervention, while the system autonomously generates and updates access rules based on user profiles and safety ranks, eliminating the need for parents to manually maintain white and black lists
Solution Approach 2:
The patent replaces the mechanical manual process of creating and maintaining white/black lists with an automated machine learning-based system. The machine learning model automatically analyzes websites, assigns safety ranks, and generates access rules, substituting the manual mechanical process with an intelligent automated system that continuously adapts to new content
2Reliability
If manual white lists and black lists are used to control website access, then basic access control is achieved, but the system requires significant knowledge and is difficult to maintain given the dynamic nature of websites
Solution Approach 1:
The system introduces safety rank as a new parameter that dynamically changes based on website content analysis. Instead of static white/black lists, the system evaluates websites on a safety rank scale and automatically adjusts access rules based on changes in safety ranks, user profiles, and time parameters, allowing the system to adapt to the dynamic nature of websites without requiring manual updates
Solution Approach 2:
The machine learning model continuously monitors website content and user access patterns, using feedback from safety evaluations and access outcomes to automatically update safety ranks and refine access rules. This feedback loop ensures the system remains effective without requiring manual intervention to track changing website content
3Adaptability or versatility
If static access control rules are used, then simple implementation is achieved, but the system cannot adapt to varying appropriateness based on age and time
Solution Approach 1:
The system transforms static access control rules into dynamic rules that automatically adapt to changing conditions. Access rules are generated based on user profile parameters (age, preferences), time parameters (day of week, time of day), and safety ranks that continuously update based on website content analysis, allowing the system to adapt to varying appropriateness requirements
Solution Approach 2:
The machine learning model serves multiple functions: it evaluates website safety, assigns safety ranks, generates access rules, and updates the data repository. This multi-functional automated system replaces the need for separate manual processes for each of these tasks, achieving high adaptability while maintaining efficient automation
Data Source
AI summary
Various embodiments provide an approach to controlled access to online content. Such control may be based on a multitude of factors including but not limited to website content, profile for the person consuming the data. In operation, machine-learning techniques are used to classify the websites based on community and social media inputs, crowd-sourced data, as well as access rules implemented by parents or system administrators. Feedback from users/admins of the system, including the instances of allowed or denied access to websites, in conjunction with other relevant parameters, is used for iterative machine-learning techniques. Embodiments may also allow for real, or near real-time, approval or denial of access to websites by registered admins.