Dynamic Access Right Prediction Using Machine Learning Models
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Solution Overview
Problem
Predicting and recommending access rights for user devices in a dynamic resource environment is challenging due to constantly changing access rights and the need for unique assignments, making it difficult to determine available access rights for users.
Innovation Solution
A system and method using models, such as machine-learning models, to generate predictions of access rights based on resource-to-resource comparisons and real-time data, with an interface to display recommended access rights that are currently available and not assigned to another user, incorporating rules for constraint-based filtering and data from user and resource data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access rights are assigned on a one-to-one basis to ensure unique access control, then access security is improved, but the complexity of predicting and managing available access rights increases
Solution Approach 1:
The patent replaces manual or rule-based access right prediction with machine learning models that automatically analyze historical assignment data, user profiles, and resource characteristics to predict suitable access rights. This substitution of mechanical prediction methods with intelligent algorithms resolves the contradiction by maintaining secure one-to-one assignment while reducing prediction complexity through automated pattern recognition.
Solution Approach 2:
The system creates virtual representations of access rights and their assignment patterns through machine learning models. By copying and analyzing historical assignment data, the system can predict future assignments without manually evaluating each possibility, thus maintaining security requirements while simplifying the prediction process through simulated pattern analysis.
2Measurement precision
If the system tracks real-time availability of access rights to ensure accurate predictions, then prediction accuracy is improved, but the computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and storing historical access right assignment data, user profiles, and resource characteristics before predictions are needed. Machine learning models are trained in advance on this prepared data, allowing the system to make accurate real-time predictions without performing complex computations at prediction time, thus balancing accuracy with system complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw data and prediction outputs. These models act as mediators that have already learned complex patterns from historical data, simplifying the real-time prediction process while maintaining high accuracy. The intermediary models handle the computational complexity separately from the prediction delivery mechanism.
3Loss of information
If the system frequently updates access right availability to reflect recent assignments, then data freshness is improved, but the computational load and processing time increase
Solution Approach 1:
The system implements periodic updates of access right availability data rather than continuous real-time updates. Machine learning models are trained on historical data at scheduled intervals, and predictions are generated periodically or on-demand. This periodic approach maintains data freshness sufficient for accurate predictions while significantly reducing computational load compared to continuous updates, thus resolving the contradiction between data freshness and processing efficiency.
Data Source
AI summary
Systems and methods may use models to generate predictions of specific access rights for users. Further, systems and methods may generate the predictions in an environment in which the availability of the specific access rights change frequently. The access rights, predicted using embodiments described herein, may be both available and associated with user affinities. An interface associated with the primary load management system may be configured to display the predicted access rights for a user operating a user device.