Listener Suitability Evaluation in Media Programs
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Solution Overview
Problem
Current media programs in call-in formats face challenges in predicting the emotional states and content quality of listeners, leading to potential disruptions or inappropriate contributions, as personnel cannot assess these aspects until the listener joins the program.
Innovation Solution
A system that evaluates listeners by processing data from their devices, including audio signals, to determine suitability and rank them based on factors like emotional tone, sentiment, and connection quality before allowing participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personnel manually evaluate listeners during call-in format, then they can determine suitability, but they cannot predict emotional states or detect inappropriate content beforehand
Solution Approach 1:
The system performs preliminary evaluation of listeners by analyzing audio signals and metadata before they join the media program. The control system receives audio signals from listeners, determines emotional states, and evaluates suitability criteria in advance, allowing personnel to make informed decisions about who to invite without waiting until the listener actually joins the program.
2Reliability
If the system evaluates multiple listeners, then the quality of contributions improves, but the complexity of the evaluation system increases
Solution Approach 1:
The system enables self-service evaluation by automatically analyzing audio signals and metadata to determine listener suitability. The control system autonomously processes audio data, identifies emotional states, and assesses criteria without requiring manual analysis of each listener, thereby reducing the complexity burden on personnel while maintaining high evaluation quality.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated electronic systems. The control system uses audio signal processing, emotional state detection algorithms, and metadata analysis to substitute for human manual assessment, enabling consistent and scalable evaluation of multiple listeners without proportionally increasing operational complexity.
3Ease of manufacture
If personnel wait until listeners join before evaluating them, then the process is simple, but disruptive or inappropriate content may already have occurred
Solution Approach 1:
The system applies preliminary anti-action by detecting and evaluating potential harmful factors before they can disrupt the media program. The control system analyzes audio signals and metadata in advance to identify listeners who may contribute inappropriate content, allowing the system to prevent such disruptions before they occur during the actual program broadcast.
Data Source
AI summary
During an episode of a media program, a creator of the media program requests that listeners join and participate in the episode. In reply, one or more listeners provide audio data to a control system of the media program, e.g., by speaking utterances that are captured by devices of the listeners. The audio data is processed to identify the users, and to determine attributes of the audio data, to identify words expressed in the audio data, and to determine features such as sentiments or opinions of the audio data. The listeners that provided the data are ranked or scored based on information regarding the respective listeners, the attributes of the audio data, or the words or sentiments. One or more of the listeners are recommended to the creator to be permitted to participate in the episode of the media program, or automatically joined in the episode.