Machine Learning Prediction for Network Data Transmission
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Solution Overview
Problem
Conventional methods for determining successful data transmission across complex computer networks are reactive and lack predictive capabilities, especially in scenarios with disparate entities and imbalanced data sets, leading to uncertainty and inefficiency in network management.
Innovation Solution
The use of machine learning models that analyze network conditions to predict successful data transmission by employing specific data structures with time-dependent features and dynamically adjusted inputs, trained using chronological data sets to account for changing network and entity performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are applied to predict communication success in mass communications across computer networks, then predictive capability is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex prediction problem into multiple components: feature extraction modules that process different data types (network metrics, entity characteristics, communication properties), separate machine learning model components, and modular prediction algorithms. This segmentation allows the system to handle complexity through organized, independent modules rather than a monolithic complex system.
Solution Approach 2:
The patent introduces intermediary components including feature extraction layers that transform raw network data into meaningful inputs, and prediction algorithms that act as mediators between the machine learning models and the final communication success predictions. These intermediaries simplify the interface between complex models and practical application.
2Loss of information
If conventional reactive systems are used to determine successful data transmission, then system simplicity is maintained, but information loss occurs about transmission success prior to transmission
Solution Approach 1:
The patent implements preliminary action by performing communication success predictions before actual data transmission occurs. The system analyzes historical network metrics, entity characteristics, and communication properties to forecast transmission outcomes in advance, allowing senders to make informed decisions about transmission timing and routing before committing resources.
Solution Approach 2:
The patent incorporates feedback mechanisms where actual transmission outcomes are fed back into the system to continuously train and improve the machine learning models. This feedback loop enables the system to learn from past communications and enhance its predictive accuracy over time, converting reactive information into proactive predictive capability.
3Measurement precision
If data structures with time-dependent features are used to handle imbalanced data sets, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies dynamics by implementing time-dependent features that evolve and adapt as new data becomes available. The data structures are designed to capture temporal patterns and changes in network conditions, entity behaviors, and communication characteristics over time. This dynamic approach allows the system to maintain measurement precision by continuously updating its understanding of the network environment.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw network data into meaningful features through extraction and processing. The system dynamically adjusts feature parameters based on time-dependent patterns, network conditions, and data balance requirements. This parameter transformation enables precise measurement of communication success probabilities while managing data complexity through structured feature engineering.
Data Source
AI summary
Methods and systems for predicting successful data transmission during mass communications across computer networks featuring disparate entities and imbalanced data sets using machine learning models. For example, the methods and systems provide a prediction as to whether or not a communication will be successful prior to the transmission being sent. Moreover, in some embodiments, the methods and systems described herein provide probability of a successful transmission as a function of time. For example, the methods and system provide a probability of how likely a communication will succeed (or fail) if it is sent at various times. Additionally, in some embodiments, the methods and systems may alert a sender prior to the transmission of a communication that the transmission is likely to succeed or fail.