Paralinguistic Content Presentation for Receptive Timing
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Solution Overview
Problem
Existing content presentation methods, such as targeted advertising, fail to consider users' emotional states and optimal presentation times, leading to annoyance and reduced receptiveness due to the use of keywords, cookies, and browsing histories that do not account for emotional or situational factors.
Innovation Solution
A method that utilizes paralinguistic features from audio input, such as acoustic aspects distinct from verbal content, to determine a user's receptiveness to product placement, selecting and presenting targeted content messages based on a predictive model's analysis of these features, including optimal timing and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content messages are presented to users based on keywords, cookies, and browsing histories, then targeted advertising effectiveness is improved, but user receptiveness deteriorates due to inconvenient timing and presentation methods
Solution Approach 1:
The patent changes the parameters for content selection from traditional demographic and behavioral data (keywords, cookies, browsing histories) to real-time paralinguistic features (pitch, volume, tempo, pauses, laughter, sighs). This parameter transformation enables timing-based targeting that adapts to user emotional states and situational contexts, resolving the contradiction by making ads more effective through better timing while reducing annoyance by presenting content when users are actually receptive.
Solution Approach 2:
The system continuously monitors paralinguistic features during user interactions and uses this feedback to dynamically determine optimal content presentation timing. The predictive model processes real-time audio data to assess user receptiveness, creating a closed-loop system that adjusts content delivery based on actual user emotional states and situational factors, thereby improving effectiveness while minimizing annoyance.
2Measurement precision
If paralinguistic features are extracted and processed in real-time, then content presentation relevance is improved, but system complexity increases
Solution Approach 1:
The patent introduces a predictive model as an intermediary component that bridges the gap between raw audio data and content selection decisions. This intermediary layer processes paralinguistic features and translates them into receptiveness predictions, simplifying the overall system architecture while maintaining high measurement precision. The model acts as a mediator that converts complex audio analysis into actionable insights for content delivery.
Solution Approach 2:
The system performs preliminary extraction and analysis of paralinguistic features during user interactions, preparing receptiveness assessments before content presentation decisions are made. By conducting this audio processing work in advance and continuously, the system reduces the computational burden during critical decision moments while maintaining accurate receptiveness measurement, thus managing system complexity effectively.
Data Source
AI summary
Embodiments disclosed herein select a content message to present to a user on a page of an application based on paralinguistic features of audio input received from the user for the application. The audio input is received via a microphone associated with a computing device. A feature extractor extracts paralinguistic features from the audio input. A predictive model determines a label indicating a measure of receptiveness to product placement (e.g., a predicted marketing outcome) based on the paralinguistic features. A content-selection component selects a content message to present to the user based on the label and based on a profile of the user.