Voice Waveform Analysis for Customized Content Recommendation
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Solution Overview
Problem
Users face difficulties in finding relevant information due to the vast amount of data available, and existing recommendation systems require user input or rely on inefficient analysis of usage history to provide personalized content.
Innovation Solution
A system and method that utilize voice recognition and waveform analysis to identify user characteristics and preferences, allowing for the provision of customized content through smart devices and a recommendation server that collects and analyzes sound data to recommend tailored information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommendation system uses user input methods to recommend information, then the system can provide personalized recommendations, but the user finds it inconvenient to provide required information
Solution Approach 1:
The system automatically analyzes the user's voice characteristics and content consumption patterns without requiring explicit user input. The user terminal collects waveform data and voice data during normal usage, and the recommendation server automatically generates interest information, making the system self-serve the user's preferences.
Solution Approach 2:
The patent replaces manual information input (mechanical interaction) with automatic voice and sound analysis. Instead of requiring users to fill out preference forms or search queries, the system uses acoustic signal processing to automatically infer user characteristics and content preferences.
2Ease of operation
If a recommendation system relies on usage history analysis, then the system can operate without user input, but the recommended information is far different from what the user actually requires
Solution Approach 1:
The patent changes the analysis parameters from simple usage history (what content was consumed) to acoustic characteristics (how the user consumed it). By analyzing voice pitch, volume, waveform patterns, and speech tempo during content consumption, the system captures nuanced user states and preferences that usage history alone cannot reveal.
Solution Approach 2:
The patent introduces acoustic signal analysis as an intermediary between usage history and user preferences. The voice and sound data serve as a mediator that translates passive content consumption into active preference signals, bridging the gap between what users do and what they actually prefer.
3Measurement precision
If the system collects and analyzes voice and sound data to generate interest information, then customized content can be provided accurately, but the system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct functional modules: the user terminal handles data collection (waveform data, voice data), the recommendation server handles interest information generation, and separate processing occurs for voice recognition and sound analysis. This modular segmentation manages system complexity while maintaining high measurement precision.
4Ease of operation
If the system uses voice recognition and waveform analysis to identify user characteristics, then customized content can be provided without user input, but the amount of data processing required increases
Solution Approach 1:
The system performs preliminary data collection and processing by continuously capturing waveform data and voice data during normal content consumption. The user terminal pre-processes this data locally before transmission, and the recommendation server maintains ready-to-analyze datasets, reducing the computational burden during actual recommendation generation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the provision of customized content that accurately reflects user interests by analyzing voice and sound data, eliminating the need for user input and improving information retrieval efficiency.
Implementation Method 1
receive a voice of a user with respect to a preset text for voice recognition to register the voice of the user as voice data for user identification
Implementation Method 2
receive waveform data of a sound output from the smart device and analyze the voice data and the waveform data
Data Source
AI summary
Disclosed are a system and a method of providing customized content by using a sound, the system including: multiple smart devices outputting content for each channel received from a content provider; multiple user terminals configured to: receive a user's voice for a preset text for voice recognition to register the user's voice as voice data for user identification, and receive waveform data of a sound output from the smart device and voice data of the user for transmission when a voice corresponding to the registered voice data for user identification is recognized while the smart device is in operation; and a recommendation server configured to: collect the waveform data for the content that is possibly output from the smart device, and analyze the voice data and the waveform data to generate interest information of the user when the waveform data and the voice data are transmitted from the user terminal.