Machine Learning Model for Invalid Access-Right Detection
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Solution Overview
Problem
Current systems face challenges in accurately detecting invalid access-right requests due to lack of data on user interactions and unavailable metadata, leading to delayed identification of invalid actions after resources are no longer available.
Innovation Solution
A machine-learning model is trained using metadata to predict invalid access-right requests through enhanced relevance scoring and learn-to-rank techniques, enabling real-time detection of invalid user activity by ranking requests and performing pairwise comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If third-party systems evaluate previously assigned access rights after the resource is no longer available, then the system can determine invalidity of past actions, but the detection time is delayed and the system interactions leading to assignment are not effectively managed
Solution Approach 1:
The machine learning model performs preliminary evaluation of access-right requests before they are processed for assignment to users. By predicting invalidity in advance using features extracted from user device interactions and metadata, the system identifies problematic requests prior to resource allocation, eliminating the need for post-hoc evaluation after resources are no longer available.
Solution Approach 2:
The system collects feedback data from user device interactions during the access-right assignment process and feeds this information into the machine learning model. This feedback loop enables continuous improvement of the prediction accuracy by training the model on actual interaction patterns, allowing real-time detection of invalid actions based on observed behavior rather than retrospective analysis.
2Productivity
If data representing user interactions and metadata is collected and used to train machine learning models, then real-time prediction of invalid requests is enabled, but the device complexity and data management requirements increase
Solution Approach 1:
The system segments the data collection process into distinct components: collecting user interaction data from user devices, extracting metadata from access-right requests, and storing these features in a centralized data repository. This segmentation allows each component to be optimized independently and simplifies the overall architecture by dividing the complex data management task into manageable modules.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw data from user interactions and the final access-right assignment decisions. This intermediary layer processes and analyzes the collected data, transforming it into predictive insights that guide the assignment process. The model mediates between data collection requirements and decision-making needs, simplifying the system by centralizing the complexity in a single trained model.
Data Source
AI summary
The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. More particularly, the present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system, train a machine-learning model to predict invalid access-right requests using the collected data set, and execute the trained machine-learning model to process new access-right requests to generate predictions as to whether or not the new access-right requests are invalid.