Dynamic Secondary Content Insertion in Live Streaming
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Solution Overview
Problem
Current methods for inserting secondary content into live streaming are statically pre-defined, leading to disruptive interruptions during climactic events in main content transmissions, which can displease users and lack personalization based on individual preferences.
Innovation Solution
A method that measures user biometric responses to detect climactic events, forecasts non-climactic periods, and dynamically inserts secondary content during these periods, using a processor and memory to ensure minimal disruption and tailoring to individual user preferences, with incentives for user participation in validating forecasts through dynamic delay adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If secondary content is inserted at statically pre-defined intervals in live streaming, then the transmission structure is simple and easy to implement, but the main content is disrupted during climactic events causing user dissatisfaction
Solution Approach 1:
The patent transforms the static, pre-defined content insertion schedule into a dynamic system that adapts to real-time content characteristics. The system continuously analyzes main content features (e.g., audio levels, visual activity, event detection) and adjusts secondary content insertion timing accordingly, preventing disruptions during climactic events while maintaining regular insertion during non-climactic periods.
Solution Approach 2:
The system implements feedback mechanisms by monitoring user responses and content engagement metrics in real-time. This feedback loop allows the system to learn from user behavior patterns and refine its content insertion decisions, improving the balance between advertising delivery and content quality over time.
2Object-affected harmful factors
If secondary content insertion timing is dynamically adjusted based on content analysis, then user satisfaction improves by avoiding climactic event disruptions, but the system complexity increases
Solution Approach 1:
The patent divides the content analysis task into multiple independent modules, each responsible for specific features (audio analysis, visual analysis, event detection). This segmentation allows parallel processing and reduces the computational burden on any single component, making the complex system more manageable and scalable.
Solution Approach 2:
The system performs preliminary analysis of main content characteristics before making insertion decisions. By pre-processing and caching content features in advance, the system reduces real-time computational requirements and enables faster decision-making during live streaming.
3Adaptability or versatility
If content insertion is personalized based on individual user preferences, then user satisfaction and engagement increase, but the processing requirements and computational load increase
Solution Approach 1:
The patent implements personalized content insertion by tailoring the insertion strategy to each user's specific preferences and behavior patterns. Different users receive customized content schedules based on their historical engagement data, device characteristics, and stated preferences, rather than applying a uniform approach to all users.
Solution Approach 2:
The system creates simplified user profiles that capture essential preference characteristics without storing or processing complete historical data. These compressed profile representations enable personalized decision-making with reduced computational overhead.
Data Source
AI summary
During a live stream of a main content to a user, a biometric response of the user is measured during a first streamed portion of the main content. The biometric response is analyzed to detect an event in the first streamed portion of the main content. The biometric response of the user during the event indicates that the event is a user-specific climactic event. Based on the analysis, a user-specific set of feature values is computed that are representative of the user-specific climactic event in the first streamed portion of the main content. A user-specific non-climactic period is forecasted in a future portion of the live stream during which a likelihood of an occurrence of any user-specific climactic event is below a threshold likelihood. A secondary content is inserted during the user-specific non-climactic period.