AI Voice Conversation Analysis Using Mode-Specific Feature Extraction
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Solution Overview
Problem
Existing voice conversation analysis systems face inefficiencies due to excessive computation and inaccuracy, as they consider all possible cases and require database inclusion for determination, leading to slow analysis and potential inaccuracies.
Innovation Solution
A voice conversation analysis apparatus and method that extracts minimum features for analysis using an artificial intelligence model, categorizing voice inputs into modes like voice phishing, advertisement, family, or acquaintance categories, and performs operations specific to each mode, such as prevention, filtering, or practice, to enhance computational speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems consider all possible cases for conversation analysis, then comprehensive coverage is achieved, but computational complexity increases and analysis speed decreases
Solution Approach 1:
The patent segments the conversation analysis process into multiple stages: first categorizing the voice input into broad types (phishing, advertisement, family, acquaintance), then applying mode-specific analysis only to relevant categories. This segmentation avoids analyzing all possible cases uniformly, thereby maintaining comprehensive coverage while improving analysis speed by focusing computational resources on relevant cases.
Solution Approach 2:
The patent performs preliminary categorization of voice inputs before conducting detailed analysis. By classifying voices into different categories and modes in advance, the system prepares the data structure needed for efficient subsequent analysis, avoiding the need to consider all possible cases from scratch during the main analysis phase.
2Measurement precision
If existing systems require database inclusion for determination, then accuracy for known cases is maintained, but new cases cannot be identified and analysis remains slow
Solution Approach 1:
The patent implements a dynamic determination mechanism that adapts based on the categorized mode. For voice phishing and advertisement modes, the system uses AI-based dynamic analysis that can identify new patterns without requiring pre-existing database entries. For family and acquaintance modes, traditional database matching is used. This dynamic approach maintains accuracy for known cases while enabling identification of new cases in appropriate modes.
Solution Approach 2:
The patent changes the analysis parameters based on the categorized mode. Different modes activate different determination strategies: some modes rely on database matching with specific parameters, while others use AI-based analysis with different parameters. This parameter adaptation allows the system to maintain accuracy for database-matched cases while gaining versatility for new cases in modes equipped with AI analysis capabilities.
3Productivity
If minimum features are extracted for analysis, then computational speed increases, but analysis accuracy may decrease
Solution Approach 1:
The patent applies local quality by extracting different feature sets based on the categorized mode. Each mode (phishing, advertisement, family, acquaintance) has its own optimized feature extraction requirements. The system extracts minimum necessary features for each specific mode rather than using a uniform comprehensive feature set, thereby improving computational speed while maintaining accuracy through mode-specific feature selection.
Data Source
AI summary
The present invention relates to a voice conversation analysis apparatus and a method therefor and, more specifically, to: a voice conversation analysis apparatus categorizing voices generated during a voice conversation so as to predict required functions and further analyzing the voices so as to provide proper functions; and a method therefor. In addition, disclosed are: an artificial intelligence (AI) system for simulating the functions of recognition, decision-making, and the like of the human brain by using a machine learning algorithm; and an application thereof. According to one embodiment, disclosed in an electronic device control method for performing an operation through a suitable operating mode by using an AI learning model so as to analyze a voice conversation, comprising the steps of: receiving a voice and acquiring information on the voice; acquiring category information on the voice on the basis of the information on the voice so as to determine at least one operating mode corresponding to the category information; and performing an operation related to the operating mode by using an AI model corresponding to the determined operating mode.