Call Classification via Feature Extraction and Generative Review
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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 method and system for classifying calls using a unique call identifier, extracting significant features, generating a call classification model, and employing a generative review model to correlate text reviews with call classes, while ensuring data security through probabilistic hashing and frequency analysis to recognize undesirable calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote data analysis is performed on user personal data, then the ability to analyze call patterns and classify calls is improved, but the security of user personal data deteriorates due to vulnerability to unauthorized access and misuse
Solution Approach 1:
The patent extracts only the essential features needed for call classification (call duration, caller ID, answer status) while leaving the device and removing unnecessary personal identifiers. This extraction approach maintains data security by not transmitting full personal data while still enabling effective call analysis and classification capabilities.
2Measurement precision
If detailed personal data is transmitted for analysis, then the accuracy of call classification is improved, but the risk of data misuse and unauthorized access increases
Solution Approach 1:
The patent applies local quality by differentiating which data elements are transmitted and which are protected. Essential classification features (duration, answer status, caller ID) are transmitted with higher quality/detail, while sensitive personal identifiers are either hashed, aggregated, or not transmitted at all. This selective data transmission maintains classification accuracy while reducing data vulnerability.
3Adaptability or versatility
If user personal data is collected and transmitted, then the ability to provide personalized services is improved, but the privacy protection of users deteriorates
Solution Approach 1:
The patent uses copying by transmitting aggregated statistics and anonymized features rather than original personal data. Call duration, answer status, and caller ID information are copied in a transformed, anonymized form that enables personalized service delivery without exposing actual user privacy information. This copying approach maintains service personalization while protecting user privacy.
Data Source
AI summary
Disclosed herein are systems and methods for classifying calls on a remote device. In one aspect, 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.