Unsupervised Learning for Target Device Profile Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network communication systems lack an efficient method to determine a target device profile, including expected behaviors, which is crucial for optimizing network communication configurations, leading to suboptimal performance and connectivity issues.
Innovation Solution
The implementation of an unsupervised machine learning algorithm to analyze global and device-type-specific datasets, identifying clusters that correlate device attributes with behaviors, and using these correlations to generate a target device profile that includes expected behaviors, such as connection times and roaming behaviors, by applying weights based on attribute and behavior types, number of devices, and time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network communication systems use traditional configuration methods without machine learning, then the system complexity remains low, but the network communication performance and connectivity are suboptimal
Solution Approach 1:
The system automatically generates device profiles and determines optimal network communication configurations by analyzing historical data and identifying patterns, eliminating the need for manual configuration. The machine learning model self-adjusts network parameters based on observed device behaviors, achieving optimal performance without human intervention.
Solution Approach 2:
The system changes network communication parameters dynamically by determining optimal values for transmission power, channel selection, and timing based on analyzed device profiles. The machine learning algorithm continuously refines these parameters by learning from historical communication data, improving performance over time.
2Measurement precision
If manual device profiling methods are used, then the implementation complexity is low, but the accuracy of expected behavior determination is insufficient
Solution Approach 1:
The system collects actual device behavior data from network communications and uses this feedback to train and refine the machine learning model. The model continuously learns from real-world performance data, improving its accuracy in predicting expected behaviors through iterative feedback loops.
Solution Approach 2:
The patent replaces manual analysis methods with automated machine learning algorithms that process large volumes of historical data to identify patterns. The neural network or decision tree models automatically extract meaningful behaviors from raw communication data, achieving high accuracy without manual intervention.
3Productivity
If network configurations are not tailored to specific device types and behaviors, then the configuration simplicity is maintained, but the connectivity and performance optimization are limited
Solution Approach 1:
The system segments devices into distinct profiles based on their behavioral patterns and characteristics. By categorizing devices into specific profiles (e.g., mobile devices, fixed devices, IoT devices), the system can apply optimized configurations tailored to each segment, improving overall network efficiency.
Solution Approach 2:
The network configuration becomes dynamic rather than static, automatically adjusting parameters based on the identified device profile and current network conditions. The system dynamically selects optimal transmission parameters, timing, and channel configurations tailored to each device's expected behavior.
Data Source
AI summary
Techniques for obtaining a target device profile included an expected behavior for a target device are disclosed. An unsupervised learning algorithm is applied to a global dataset including device data corresponding to multiple device types. Additionally, the unsupervised learning algorithm is applied to a device type dataset including device data corresponding to a single device type. Clusters are obtained from both the global dataset and the device type dataset. Clusters that share a device attribute with a target device are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device. Values, for a particular behavior that is common to multiple relevant clusters, are merged to determine an expected value, for the particular behavior, for the target device. Additionally or alternatively, the behaviors, associated with the relevant clusters, are aggregated to form the target device profile.