ML Spam Detection Component for Wireless Network Call Screening
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Solution Overview
Problem
Current methods for detecting spam calls in wireless networks are inadequate, as they often rely solely on the caller's telephone number and lack advanced analysis techniques, leading to ineffective filtering and resource wastage.
Innovation Solution
Implementing machine learning and artificial intelligence techniques within wireless networks to screen calls by analyzing audio features, voice recognition, and Natural Language Processing, using a Machine Learning Spam Detection Component to classify calls as spam or not, and refine models based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are implemented for spam call detection, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
A Machine Learning Spam Detection Component is introduced as an intermediary between the call processing system and the user notification system. This component receives call information, performs ML-based analysis of audio features and voice patterns, and generates spam probability scores, thereby improving detection accuracy while isolating the complexity within a dedicated module.
Solution Approach 2:
The spam detection system is segmented into distinct functional modules: call information reception, ML model processing, spam probability calculation, and notification generation. This segmentation allows the complex ML operations to be confined to specific components, improving overall system manageability and detection precision simultaneously.
2Measurement precision
If advanced audio analysis is performed on all calls, then spam detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial audio analysis by extracting only the most relevant features (audio characteristics, voice recognition data, NLP elements) rather than analyzing complete call content. This selective approach maintains high detection accuracy while significantly reducing processing time compared to comprehensive analysis.
Solution Approach 2:
The ML models are pre-trained on extensive spam call datasets before deployment, enabling them to quickly classify new calls based on learned patterns. This preliminary training allows the system to perform rapid inference during actual call processing without requiring extensive real-time analysis.
3Measurement precision
If machine learning models are continuously refined using user feedback, then model precision is improved, but use of energy increases
Solution Approach 1:
The system implements periodic model refinement rather than continuous updates, using user feedback accumulated over defined time intervals. This approach allows the ML models to improve precision through iterative training while consuming computational energy only at scheduled update intervals, rather than continuously during normal operation.
Data Source
AI summary
A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.