AI-Based Resource Request Authorization System
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Solution Overview
Problem
Computer systems face challenges in determining the authenticity of resource requests due to complex authorization rules and potential inaccuracies in request data.
Innovation Solution
A computer-implemented method that uses a machine-learning model to process resource requests by generating key-value pairs from request data and augmenting data, allowing for the identification of indicators that may threaten operational stability and flagging entities for resource access inhibition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authorization rules are used to verify resource requests, then the system can process requests with simple authorization logic, but the system becomes vulnerable to inaccurate or malicious requests that threaten operational stability
Solution Approach 1:
The patent introduces an intermediary authorization service that mediates between resource requests and resources. This service uses machine learning models to analyze request patterns, augment request data with additional context, and make intelligent authorization decisions. The intermediary layer handles the complexity of threat detection and authorization verification, protecting the core system while maintaining operational stability.
Solution Approach 2:
The patent replaces traditional mechanical authorization rules with machine learning-based intelligent authorization. Instead of relying on predefined static rules, the system uses trained machine learning models that can dynamically analyze request patterns, detect anomalies, and make authorization decisions based on learned behaviors, thereby improving reliability against sophisticated threats.
2Measurement precision
If the system processes all resource requests through detailed authorization analysis, then authorization accuracy improves, but processing speed decreases
Solution Approach 1:
The patent applies partial action by using machine learning models to prioritize authorization analysis. Instead of analyzing all requests equally, the system identifies and focuses computational resources on requests that exhibit suspicious patterns or higher risk characteristics. The machine learning model scores requests and applies more rigorous analysis only where needed, maintaining both accuracy and processing speed.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on historical request data before deployment. This preliminary training enables the models to quickly identify suspicious patterns during runtime without requiring extensive real-time analysis of every request. The pre-computed knowledge allows for fast, accurate authorization decisions.
3Measurement precision
If the system collects and analyzes extensive augmenting data from external sources, then detection accuracy of malicious requests improves, but data processing complexity and time increase
Solution Approach 1:
The patent extracts only the most relevant features and data points from augmenting data sources for inclusion in the machine learning model training and inference. Instead of processing all available data, the system identifies and extracts key indicators that are most predictive of malicious behavior, reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The system changes parameters by transforming raw augmenting data into standardized features suitable for machine learning processing. This includes normalization, feature engineering, and selecting only the most informative parameters. By changing the form and selection of data parameters, the system reduces processing complexity while preserving the information needed for accurate threat detection.
Data Source
AI summary
A set of resource requests that each includes authorization-supporting data for receiving a requested resource can be received. For each request, augmenting data associated with part of the data is retrieved, and it is determined whether access is authorized based on the augmenting data and the authorization-supporting data. A machine-learning model is trained using representations of the set of resource requests and the authorization determinations. Additional requests are processed by the trained model to generate corresponding authorization outputs. One or more identifiers to flag for inhibition of resource access are determined based on the authorization outputs. Upon detecting that a new resource request to access a particular resource includes an identifier of the one or more identifiers, a new authorization output is generated to inhibit access to the particular resource.