Contextual ML Models for Access-Right Reassignment Value Prediction
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Solution Overview
Problem
Existing systems face challenges in efficiently and accurately determining the value components for reassigning access rights to resources, particularly due to computationally inefficient data collection and evaluation processes, leading to sub-optimal value determination.
Innovation Solution
Implementing a contextual machine-learning model and reinforcement learning techniques to intelligently predict the value components of reassignment conditions, using a value determination system that continuously updates and adapts based on real-time feedback and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data collection and evaluation methods are used to determine value components, then data can be collected and processed, but the process is computationally inefficient and excessively time-consuming
Solution Approach 1:
The patent replaces traditional mechanical data collection and evaluation systems with machine learning models that can process and analyze data automatically. The machine learning model substitutes the manual or traditional computational processes for determining value components, enabling faster and more efficient value determination without excessive time consumption.
Solution Approach 2:
The machine learning model performs self-learning and self-adjustment to continuously improve value component predictions. The system automatically collects data, processes it through the model, and refines its predictions without requiring extensive external intervention or manual evaluation, thereby improving productivity while reducing time loss.
2Measurement precision
If traditional approaches are used for value determination with big data, then data can be processed, but the approach is not computationally efficient and may not generate accurate values
Solution Approach 1:
The patent replaces complex traditional computational approaches with machine learning models that are better suited for processing big data. The machine learning model simplifies the computational complexity by using learned patterns and relationships from training data, thereby improving both accuracy and computational efficiency simultaneously.
Solution Approach 2:
The machine learning model is trained in advance on historical data to learn the relationships and patterns that determine value components. This preliminary training action enables the model to make accurate predictions during actual value determination without requiring complex real-time computations, thus improving both accuracy and reducing computational complexity.
3Productivity
If data is collected or aggregated once a month, then data collection can be performed, but the delayed data collection causes sub-optimal value determination
Solution Approach 1:
The machine learning model enables continuous value determination by processing data as it becomes available rather than waiting for monthly aggregations. The model continuously learns from incoming data and provides real-time or near-real-time value component predictions, maintaining both high productivity and reliability by eliminating delays in data collection and evaluation.
Data Source
AI summary
The present disclosure generally relates to systems and methods that intelligently generate reassignment value condition for reassigning access rights. The systems and methods include executing a trained contextual machine-learning model to generate predictions of value components of the reassignment value condition, which once satisfied, enables an access-right requestor to have an assigned access right reassigned to the access-right requestor.