Interaction Prediction System Using Entity Vectorization
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Solution Overview
Problem
Traditional methods for predicting future interactions are inaccurate, require multiple models, and overload 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 log10(X) = m1*B*m2 + c, where X is the estimated interaction resources, and vectors and matrices are determined using historical interactions to predict interactions between entities, reducing data and calculation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to predict future interactions, then multiple models are required to handle different entities, but this increases device complexity and processing requirements
Solution Approach 1:
The patent applies universality by creating a single interaction prediction model that can handle multiple different entities through vectorization. Instead of requiring separate models for different entity types (as in traditional methods), the system transforms all entities into numerical vectors that can be processed by one unified model, thereby reducing device complexity while maintaining prediction accuracy across diverse entities
Solution Approach 2:
The patent applies parameter changes by transforming entity characteristics into numerical vector representations. This parameter transformation allows the system to process different entities using a common mathematical framework, eliminating the need for multiple specialized models and reducing overall system complexity while preserving the ability to accurately predict interactions
2Reliability
If traditional methods are used to predict future interactions, then large processing and memory requirements are needed, but this reduces productivity
Solution Approach 1:
The patent applies the extraction principle by isolating the essential interaction patterns from complex entity data and representing them as compact numerical vectors. This extraction of core features eliminates unnecessary computational overhead while retaining the critical information needed for accurate interaction prediction, thereby improving processing capacity without sacrificing reliability
Solution Approach 2:
The patent applies copying by creating simplified numerical vector representations of entities that capture their essential interaction characteristics. These vector copies allow the system to process entity interactions efficiently without requiring the full complexity of original entity data, reducing memory and processing requirements while maintaining prediction accuracy
3Reliability
If traditional methods are used to identify authorized interactions, then system security is compromised due to inaccuracies, but improving accuracy increases processing demands
Solution Approach 1:
The patent applies parameter changes by transforming interaction data into a vector-based numerical representation that enables more efficient computation. This parameter transformation allows the system to achieve higher interaction identification accuracy with reduced processing energy, as the vectorized model can process interactions more efficiently than traditional methods while improving reliability
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.