Interaction Vectorization for Prediction Accuracy and Processing Load Reduction
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Solution Overview
Problem
Traditional methods for predicting future interactions are inaccurate, require multiple models, and overwhelm systems with high processing and memory demands, making it difficult to identify authorized or potential interactions effectively.
Innovation Solution
An interaction prediction system that uses a simplified equation (logy(X) = m1*B*m2 + c) to define entities based on interaction vectors and matrices, reducing data and calculation requirements, and employs filters like RLS to monitor interactions and prevent misappropriation by identifying anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict future interactions, then prediction accuracy is improved, but processing requirements and memory demands increase significantly
Solution Approach 1:
The patent transforms the prediction model by changing parameters from traditional complex models to a simplified interaction equation with dimensional vectors and matrices. This parameter transformation reduces computational complexity while maintaining prediction accuracy for identifying authorized and unauthorized interactions.
Solution Approach 2:
The patent extracts the essential features from complex traditional models by using a simplified interaction equation that focuses on key parameters (entities, interaction types, resources) while removing unnecessary computational overhead, thereby reducing processing requirements while maintaining predictive capability.
2Measurement precision
If traditional methods are used to predict future interactions, then prediction accuracy is improved, but memory requirements increase significantly
Solution Approach 1:
The patent reduces memory requirements by changing the parameter representation from traditional complex models to dimensional vectors and matrices. This parameter transformation compresses the data structure while preserving the essential information needed for accurate interaction prediction.
Solution Approach 2:
The patent extracts only the necessary information for prediction by using a simplified interaction equation that processes only essential parameters (entities, interaction types, resources), thereby reducing memory footprint while maintaining prediction accuracy.
3Difficulty of detecting and measuring
If traditional methods are used to monitor interactions, then detection capability is improved, but system overload increases
Solution Approach 1:
The patent implements a feedback mechanism where the interaction equation continuously processes monitored interactions and updates predictions in real-time. This feedback loop enables the system to detect anomalies and unauthorized interactions while maintaining efficient processing through the simplified mathematical model.
Solution Approach 2:
The patent changes the monitoring parameters from complex traditional models to a simplified interaction equation with dimensional vectors and matrices, enabling efficient real-time processing while maintaining high detection capability for authorized and unauthorized interactions.
Data Source
AI summary
An interaction prediction system for accurately predicting the occurrence of interactions, entities associated with the interactions, and/or resources involved with the interactions. The interaction predictions can be used for a number of different purposes, such as improving security of systems, predicting future interactions or the likelihood thereof, or the like. The interaction prediction system described herein more accurately predict the interactions using modeling and monitoring that increases the processing speeds by reducing the data needed to make the predictions, reduces the memory requirements to make the predictions, and increases the capacity of the processing systems when compared to traditional systems.