Call Classification Using Feature Extraction and Probabilistic Hashing
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Solution Overview
Problem
Existing methods for classifying calls lack accuracy and security, particularly in remote data analysis, where user personal data is vulnerable to unauthorized access and misuse.
Innovation Solution
A system and method that collect call data, extract significant features, generate classification models, and use generative review models to classify calls, incorporating probabilistic hashing for depersonalization and frequency analysis to identify suspicious calls, ensuring secure data handling and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote data analysis is performed on user personal data, then data processing capability is improved, but data security deteriorates
Solution Approach 1:
The patent extracts only the essential features needed for call classification (call duration, time of day, day of week, call type) from the complete call data, transmitting only these extracted features to remote servers for analysis while leaving sensitive personal data on the user's device
Solution Approach 2:
The patent creates simplified copies of call data in the form of statistical features and patterns rather than transmitting complete raw data, allowing remote analysis to be performed on representative data samples that do not contain sensitive personal information
2Measurement precision
If comprehensive call data is collected for classification, then classification accuracy is improved, but data transmission volume increases
Solution Approach 1:
The system extracts only the most significant features from comprehensive call data (duration, timing, call type indicators) that are sufficient for accurate classification while excluding redundant information, thereby reducing transmission volume without sacrificing classification accuracy
Solution Approach 2:
The patent applies different processing approaches to different data elements, performing local feature extraction and filtering on the user device before transmission, sending only the processed essential features rather than raw comprehensive data
Data Source
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AI summary
Disclosed herein are systems and methods for classifying calls on a remote device. In one example, an exemplary method comprises, collecting call data for each call, wherein each call is associated with a unique call identifier, extracting significant features from the collected call data, generating a call classification model based on the extracted significant features, wherein the call classification model comprises a set of rules based on which a predetermined call class is assigned to the call, extracting a text review from the collected call data, generating a generative review model based on the extracted text review, the generative review model used for correlating text reviews with a call class, and classifying the call for which the call data was collected based on the call classification model generated and the generative review model.